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2407.05627
|
New Directions in Text Classification Research: Maximizing The
Performance of Sentiment Classification from Limited Data
|
The stakeholders' needs in sentiment analysis for various issues, whether positive or negative, are speed and accuracy. One new challenge in sentiment analysis tasks is the limited training data, which often leads to suboptimal machine learning models and poor performance on test data. This paper discusses the problem of text classification based on limited training data (300 to 600 samples) into three classes: positive, negative, and neutral. A benchmark dataset is provided for training and testing data on the issue of Kaesang Pangarep's appointment as Chairman of PSI. External data for aggregation and augmentation purposes are provided, consisting of two datasets: the topic of Covid Vaccination sentiment and an open topic. The official score used is the F1-score, which balances precision and recall among the three classes, positive, negative, and neutral. A baseline score is provided as a reference for researchers for unoptimized classification methods. The optimized score is provided as a reference for the target score to be achieved by any proposed method. Both scoring (baseline and optimized) use the SVM method, which is widely reported as the state-of-the-art in conventional machine learning methods. The F1-scores achieved by the baseline and optimized methods are 40.83% and 51.28%, respectively.
|
http://arxiv.org/pdf/2407.05627v1
|
[
"Surya Agustian",
"Muhammad Irfan Syah",
"Nurul Fatiara",
"Rahmad Abdillah"
] |
2024-07-08T05:42:29Z
|
2024-07-08T05:42:29Z
|
2310.17110
|
LLM4DyG: Can Large Language Models Solve Spatial-Temporal Problems on
Dynamic Graphs?
|
In an era marked by the increasing adoption of Large Language Models (LLMs) for various tasks, there is a growing focus on exploring LLMs' capabilities in handling web data, particularly graph data. Dynamic graphs, which capture temporal network evolution patterns, are ubiquitous in real-world web data. Evaluating LLMs' competence in understanding spatial-temporal information on dynamic graphs is essential for their adoption in web applications, which remains unexplored in the literature. In this paper, we bridge the gap via proposing to evaluate LLMs' spatial-temporal understanding abilities on dynamic graphs, to the best of our knowledge, for the first time. Specifically, we propose the LLM4DyG benchmark, which includes nine specially designed tasks considering the capability evaluation of LLMs from both temporal and spatial dimensions. Then, we conduct extensive experiments to analyze the impacts of different data generators, data statistics, prompting techniques, and LLMs on the model performance. Finally, we propose Disentangled Spatial-Temporal Thoughts (DST2) for LLMs on dynamic graphs to enhance LLMs' spatial-temporal understanding abilities. Our main observations are: 1) LLMs have preliminary spatial-temporal understanding abilities on dynamic graphs, 2) Dynamic graph tasks show increasing difficulties for LLMs as the graph size and density increase, while not sensitive to the time span and data generation mechanism, 3) the proposed DST2 prompting method can help to improve LLMs' spatial-temporal understanding abilities on dynamic graphs for most tasks. The data and codes are publicly available at Github.
|
http://arxiv.org/pdf/2310.17110v3
|
[
"Zeyang Zhang",
"Xin Wang",
"Ziwei Zhang",
"Haoyang Li",
"Yijian Qin",
"Wenwu Zhu"
] |
2024-07-08T05:39:38Z
|
2023-10-26T02:37:43Z
|
2407.05622
|
On the Complexity of Learning Sparse Functions with Statistical and
Gradient Queries
|
The goal of this paper is to investigate the complexity of gradient algorithms when learning sparse functions (juntas). We introduce a type of Statistical Queries ($mathsf{SQ}$), which we call Differentiable Learning Queries ($mathsf{DLQ}$), to model gradient queries on a specified loss with respect to an arbitrary model. We provide a tight characterization of the query complexity of $mathsf{DLQ}$ for learning the support of a sparse function over generic product distributions. This complexity crucially depends on the loss function. For the squared loss, $mathsf{DLQ}$ matches the complexity of Correlation Statistical Queries $(mathsf{CSQ})$--potentially much worse than $mathsf{SQ}$. But for other simple loss functions, including the $ell_1$ loss, $mathsf{DLQ}$ always achieves the same complexity as $mathsf{SQ}$. We also provide evidence that $mathsf{DLQ}$ can indeed capture learning with (stochastic) gradient descent by showing it correctly describes the complexity of learning with a two-layer neural network in the mean field regime and linear scaling.
|
http://arxiv.org/pdf/2407.05622v1
|
[
"Nirmit Joshi",
"Theodor Misiakiewicz",
"Nathan Srebro"
] |
2024-07-08T05:30:34Z
|
2024-07-08T05:30:34Z
|
2407.03146
|
Enhancing Class Fairness in Classification with A Two-Player Game
Approach
|
Data augmentation is widely applied and has shown its benefits in different machine learning tasks. However, as recently observed in some downstream tasks, data augmentation may introduce an unfair impact on classifications. While it can improve the performance of some classes, it can actually be detrimental for other classes, which can be problematic in some application domains. In this paper, to counteract this phenomenon, we propose a FAir Classification approach with a Two-player game (FACT). We first formulate the training of a classifier with data augmentation as a fair optimization problem, which can be further written as an adversarial two-player game. Following this formulation, we propose a novel multiplicative weight optimization algorithm, for which we theoretically prove that it can converge to a solution that is fair over classes. Interestingly, our formulation also reveals that this fairness issue over classes is not due to data augmentation only, but is in fact a general phenomenon. Our empirical experiments demonstrate that the performance of our learned classifiers is indeed more fairly distributed over classes in five datasets, with only limited impact on the average accuracy.
|
http://arxiv.org/pdf/2407.03146v2
|
[
"Yunpeng Jiang",
"Paul Weng",
"Yutong Ban"
] |
2024-07-08T05:21:59Z
|
2024-05-31T02:56:43Z
|
2407.05615
|
OSN: Infinite Representations of Dynamic 3D Scenes from Monocular Videos
|
It has long been challenging to recover the underlying dynamic 3D scene representations from a monocular RGB video. Existing works formulate this problem into finding a single most plausible solution by adding various constraints such as depth priors and strong geometry constraints, ignoring the fact that there could be infinitely many 3D scene representations corresponding to a single dynamic video. In this paper, we aim to learn all plausible 3D scene configurations that match the input video, instead of just inferring a specific one. To achieve this ambitious goal, we introduce a new framework, called OSN. The key to our approach is a simple yet innovative object scale network together with a joint optimization module to learn an accurate scale range for every dynamic 3D object. This allows us to sample as many faithful 3D scene configurations as possible. Extensive experiments show that our method surpasses all baselines and achieves superior accuracy in dynamic novel view synthesis on multiple synthetic and real-world datasets. Most notably, our method demonstrates a clear advantage in learning fine-grained 3D scene geometry. Our code and data are available at https://github.com/vLAR-group/OSN
|
http://arxiv.org/pdf/2407.05615v1
|
[
"Ziyang Song",
"Jinxi Li",
"Bo Yang"
] |
2024-07-08T05:03:46Z
|
2024-07-08T05:03:46Z
|
2406.12837
|
LayerMerge: Neural Network Depth Compression through Layer Pruning and
Merging
|
Recent works show that reducing the number of layers in a convolutional neural network can enhance efficiency while maintaining the performance of the network. Existing depth compression methods remove redundant non-linear activation functions and merge the consecutive convolution layers into a single layer. However, these methods suffer from a critical drawback; the kernel size of the merged layers becomes larger, significantly undermining the latency reduction gained from reducing the depth of the network. We show that this problem can be addressed by jointly pruning convolution layers and activation functions. To this end, we propose LayerMerge, a novel depth compression method that selects which activation layers and convolution layers to remove, to achieve a desired inference speed-up while minimizing performance loss. Since the corresponding selection problem involves an exponential search space, we formulate a novel surrogate optimization problem and efficiently solve it via dynamic programming. Empirical results demonstrate that our method consistently outperforms existing depth compression and layer pruning methods on various network architectures, both on image classification and generation tasks. We release the code at https://github.com/snu-mllab/LayerMerge.
|
http://arxiv.org/pdf/2406.12837v3
|
[
"Jinuk Kim",
"Marwa El Halabi",
"Mingi Ji",
"Hyun Oh Song"
] |
2024-07-08T04:55:34Z
|
2024-06-18T17:55:15Z
|
2407.05593
|
Unmasking Trees for Tabular Data
|
We herein describe UnmaskingTrees, a method and open-source software package for tabular data generation and, especially, imputation. Our experiments suggest that training gradient-boosted trees to incrementally unmask features offers a simple, strong baseline for imputation.
|
http://arxiv.org/pdf/2407.05593v1
|
[
"Calvin McCarter"
] |
2024-07-08T04:15:43Z
|
2024-07-08T04:15:43Z
|
2407.07921
|
A Trustworthy AIoT-enabled Localization System via Federated Learning
and Blockchain
|
There is a significant demand for indoor localization technology in smart buildings, and the most promising solution in this field is using RF sensors and fingerprinting-based methods that employ machine learning models trained on crowd-sourced user data gathered from IoT devices. However, this raises security and privacy issues in practice. Some researchers propose to use federated learning to partially overcome privacy problems, but there still remain security concerns, e.g., single-point failure and malicious attacks. In this paper, we propose a framework named DFLoc to achieve precise 3D localization tasks while considering the following two security concerns. Particularly, we design a specialized blockchain to decentralize the framework by distributing the tasks such as model distribution and aggregation which are handled by a central server to all clients in most previous works, to address the issue of the single-point failure for a reliable and accurate indoor localization system. Moreover, we introduce an updated model verification mechanism within the blockchain to alleviate the concern of malicious node attacks. Experimental results substantiate the framework's capacity to deliver accurate 3D location predictions and its superior resistance to the impacts of single-point failure and malicious attacks when compared to conventional centralized federated learning systems.
|
http://arxiv.org/pdf/2407.07921v1
|
[
"Junfei Wang",
"He Huang",
"Jingze Feng",
"Steven Wong",
"Lihua Xie",
"Jianfei Yang"
] |
2024-07-08T04:14:19Z
|
2024-07-08T04:14:19Z
|
2407.05591
|
On the Power of Convolution Augmented Transformer
|
The transformer architecture has catalyzed revolutionary advances in language modeling. However, recent architectural recipes, such as state-space models, have bridged the performance gap. Motivated by this, we examine the benefits of Convolution-Augmented Transformer (CAT) for recall, copying, and length generalization tasks. CAT incorporates convolutional filters in the K/Q/V embeddings of an attention layer. Through CAT, we show that the locality of the convolution synergizes with the global view of the attention. Unlike comparable architectures, such as Mamba or transformer, CAT can provably solve the associative recall (AR) and copying tasks using a single layer while also enjoying guaranteed length generalization. We also establish computational tradeoffs between convolution and attention by characterizing how convolution can mitigate the need for full attention by summarizing the context window and creating salient summary tokens to attend. Evaluations on real datasets corroborate our findings and demonstrate that CAT and its variations indeed enhance the language modeling performance.
|
http://arxiv.org/pdf/2407.05591v1
|
[
"Mingchen Li",
"Xuechen Zhang",
"Yixiao Huang",
"Samet Oymak"
] |
2024-07-08T04:08:35Z
|
2024-07-08T04:08:35Z
|
2310.01991
|
Fill in the Blank: Exploring and Enhancing LLM Capabilities for Backward
Reasoning in Math Word Problems
|
While forward reasoning (i.e., find the answer given the question) has been explored extensively in recent literature, backward reasoning is relatively unexplored. We examine the backward reasoning capabilities of LLMs on Math Word Problems (MWPs): given a mathematical question and its answer, with some details omitted from the question, can LLMs effectively retrieve the missing information? On modifying three benchmark datasets for this task, to evaluate this task: GSM8k, SVAMP, and MultiArith, we find a significant drop in the accuracy of models on this task compared to forward reasoning across SOTA LLMs (GPT4, GPT3.5, PaLM-2, and LLaMa). Motivated by the fact backward reasoning can be seen as the ''inverse'' of forward reasoning, we propose variations of three different forward reasoning strategies to improve performance. Rephrase reformulates the given problem into a forward reasoning problem, PAL-Tools combines the idea of Program-Aided LLMs to produce a set of equations that can be solved by an external solver, and Check your Work exploits the availability of natural verifier of high accuracy in the forward direction, interleaving solving and verification steps. Finally, realizing that each of our base methods correctly solves a different set of problems, we propose a novel Bayesian formulation for creating an ensemble over the base methods to further boost the accuracy. Extensive experimentation demonstrates successive improvement in the performance of LLMs on the backward reasoning task, using our strategies, with our ensemble-based method resulting in significant performance gains compared to the SOTA forward reasoning strategies we adapt.
|
http://arxiv.org/pdf/2310.01991v2
|
[
"Aniruddha Deb",
"Neeva Oza",
"Sarthak Singla",
"Dinesh Khandelwal",
"Dinesh Garg",
"Parag Singla"
] |
2024-07-08T03:33:43Z
|
2023-10-03T12:03:06Z
|
2407.05580
|
$\mathrm{E^{2}CFD}$: Towards Effective and Efficient Cost Function
Design for Safe Reinforcement Learning via Large Language Model
|
Different classes of safe reinforcement learning algorithms have shown satisfactory performance in various types of safety requirement scenarios. However, the existing methods mainly address one or several classes of specific safety requirement scenario problems and cannot be applied to arbitrary safety requirement scenarios. In addition, the optimization objectives of existing reinforcement learning algorithms are misaligned with the task requirements. Based on the need to address these issues, we propose $mathrm{E^{2}CFD}$, an effective and efficient cost function design framework. $mathrm{E^{2}CFD}$ leverages the capabilities of a large language model (LLM) to comprehend various safety scenarios and generate corresponding cost functions. It incorporates the textit{fast performance evaluation (FPE)} method to facilitate rapid and iterative updates to the generated cost function. Through this iterative process, $mathrm{E^{2}CFD}$ aims to obtain the most suitable cost function for policy training, tailored to the specific tasks within the safety scenario. Experiments have proven that the performance of policies trained using this framework is superior to traditional safe reinforcement learning algorithms and policies trained with carefully designed cost functions.
|
http://arxiv.org/pdf/2407.05580v1
|
[
"Zepeng Wang",
"Chao Ma",
"Linjiang Zhou",
"Libing Wu",
"Lei Yang",
"Xiaochuan Shi",
"Guojun Peng"
] |
2024-07-08T03:30:25Z
|
2024-07-08T03:30:25Z
|
2401.06692
|
An Experimental Design Framework for Label-Efficient Supervised
Finetuning of Large Language Models
|
Supervised finetuning (SFT) on instruction datasets has played a crucial role in achieving the remarkable zero-shot generalization capabilities observed in modern large language models (LLMs). However, the annotation efforts required to produce high quality responses for instructions are becoming prohibitively expensive, especially as the number of tasks spanned by instruction datasets continues to increase. Active learning is effective in identifying useful subsets of samples to annotate from an unlabeled pool, but its high computational cost remains a barrier to its widespread applicability in the context of LLMs. To mitigate the annotation cost of SFT and circumvent the computational bottlenecks of active learning, we propose using experimental design. Experimental design techniques select the most informative samples to label, and typically maximize some notion of uncertainty and/or diversity. In our work, we implement a framework that evaluates several existing and novel experimental design techniques and find that these methods consistently yield significant gains in label efficiency with little computational overhead. On generative tasks, our methods achieve the same generalization performance with only $50%$ of annotation cost required by random sampling.
|
http://arxiv.org/pdf/2401.06692v3
|
[
"Gantavya Bhatt",
"Yifang Chen",
"Arnav M. Das",
"Jifan Zhang",
"Sang T. Truong",
"Stephen Mussmann",
"Yinglun Zhu",
"Jeffrey Bilmes",
"Simon S. Du",
"Kevin Jamieson",
"Jordan T. Ash",
"Robert D. Nowak"
] |
2024-07-08T02:52:05Z
|
2024-01-12T16:56:54Z
|
2407.04495
|
Speed-accuracy trade-off for the diffusion models: Wisdom from
nonequilibrium thermodynamics and optimal transport
|
We discuss a connection between a generative model, called the diffusion model, and nonequilibrium thermodynamics for the Fokker-Planck equation, called stochastic thermodynamics. Based on the techniques of stochastic thermodynamics, we derive the speed-accuracy trade-off for the diffusion models, which is a trade-off relationship between the speed and accuracy of data generation in diffusion models. Our result implies that the entropy production rate in the forward process affects the errors in data generation. From a stochastic thermodynamic perspective, our results provide quantitative insight into how best to generate data in diffusion models. The optimal learning protocol is introduced by the conservative force in stochastic thermodynamics and the geodesic of space by the 2-Wasserstein distance in optimal transport theory. We numerically illustrate the validity of the speed-accuracy trade-off for the diffusion models with different noise schedules such as the cosine schedule, the conditional optimal transport, and the optimal transport.
|
http://arxiv.org/pdf/2407.04495v2
|
[
"Kotaro Ikeda",
"Tomoya Uda",
"Daisuke Okanohara",
"Sosuke Ito"
] |
2024-07-08T02:48:15Z
|
2024-07-05T13:35:14Z
|
2407.04308
|
SSP-GNN: Learning to Track via Bilevel Optimization
|
We propose a graph-based tracking formulation for multi-object tracking (MOT) where target detections contain kinematic information and re-identification features (attributes). Our method applies a successive shortest paths (SSP) algorithm to a tracking graph defined over a batch of frames. The edge costs in this tracking graph are computed via a message-passing network, a graph neural network (GNN) variant. The parameters of the GNN, and hence, the tracker, are learned end-to-end on a training set of example ground-truth tracks and detections. Specifically, learning takes the form of bilevel optimization guided by our novel loss function. We evaluate our algorithm on simulated scenarios to understand its sensitivity to scenario aspects and model hyperparameters. Across varied scenario complexities, our method compares favorably to a strong baseline.
|
http://arxiv.org/pdf/2407.04308v2
|
[
"Griffin Golias",
"Masa Nakura-Fan",
"Vitaly Ablavsky"
] |
2024-07-08T02:37:44Z
|
2024-07-05T07:23:51Z
|
2312.00700
|
GIFT: Generative Interpretable Fine-Tuning
|
We present Generative Interpretable Fine-Tuning (GIFT) for parameter-efficient fine-tuning of pretrained Transformer backbones, which can be formulated as a simple factorized matrix multiplication in the parameter space or equivalently in the activation/representation space, and thus embraces built-in interpretability. For a layer with weights $omegain mathbb{R}^{d_{out}times d_{in}}$, our proposed GIFT learns the fine-tuned weights $hat{omega}$ directly from $omega$ as $hat{omega}=omegacdot (mathbb{I}+phi_{d_{in}times r}cdotpsi_{rtimes d_{in}})$. $Theta=(phi, psi)$ are the learnable parameters of the two linear layers. $Theta$ can be shared by all layers selected for fine-tuning (e.g., all the Query and Value layers), or can be layer-type specific (e.g., different $Theta$'s used for Query and Value), resulting in significantly fewer trainable parameters compared to layer-specific Low-Rank Adaptation (LoRA). We perform comprehensive evaluations on natural language tasks (commonsense and arithmetic reasoning, instruction tuning, and sequence classification), and fine-grained visual classification tasks. We obtain the best performance and parameter efficiency among baselines on commonsense reasoning, instruction tuning and visual recognition benchmarks. Compared to LoRA, we obtain 5.9% absolute increase in average accuracy with 53.8 times reduction of parameters on Commonsense170k using Llama-3 (8B), and 5.4% absolute increase in the win rate with 4 times reduction of parameters using Llama-2 (7B) during instruction tuning. Our GIFT also obtains a slightly higher win rate on instruction tuning than GPT 3.5 (Turbo 1106). We show the output of the first linear layer (i.e., $omegacdot phi$) is surprisingly interpretable, which can play the role of a token-clustering head as a by-product to localize meaningful objects/parts in images for computer vision tasks.
|
http://arxiv.org/pdf/2312.00700v3
|
[
"Chinmay Savadikar",
"Xi Song",
"Tianfu Wu"
] |
2024-07-08T01:59:10Z
|
2023-12-01T16:33:57Z
|
2407.03125
|
Foundations and Frontiers of Graph Learning Theory
|
Recent advancements in graph learning have revolutionized the way to understand and analyze data with complex structures. Notably, Graph Neural Networks (GNNs), i.e. neural network architectures designed for learning graph representations, have become a popular paradigm. With these models being usually characterized by intuition-driven design or highly intricate components, placing them within the theoretical analysis framework to distill the core concepts, helps understand the key principles that drive the functionality better and guide further development. Given this surge in interest, this article provides a comprehensive summary of the theoretical foundations and breakthroughs concerning the approximation and learning behaviors intrinsic to prevalent graph learning models. Encompassing discussions on fundamental aspects such as expressiveness power, generalization, optimization, and unique phenomena such as over-smoothing and over-squashing, this piece delves into the theoretical foundations and frontier driving the evolution of graph learning. In addition, this article also presents several challenges and further initiates discussions on possible solutions.
|
http://arxiv.org/pdf/2407.03125v2
|
[
"Yu Huang",
"Min Zhou",
"Menglin Yang",
"Zhen Wang",
"Muhan Zhang",
"Jie Wang",
"Hong Xie",
"Hao Wang",
"Defu Lian",
"Enhong Chen"
] |
2024-07-08T01:22:37Z
|
2024-07-03T14:07:41Z
|
2311.09402
|
Synthetically Enhanced: Unveiling Synthetic Data's Potential in Medical
Imaging Research
|
Chest X-rays (CXR) are essential for diagnosing a variety of conditions, but when used on new populations, model generalizability issues limit their efficacy. Generative AI, particularly denoising diffusion probabilistic models (DDPMs), offers a promising approach to generating synthetic images, enhancing dataset diversity. This study investigates the impact of synthetic data supplementation on the performance and generalizability of medical imaging research. The study employed DDPMs to create synthetic CXRs conditioned on demographic and pathological characteristics from the CheXpert dataset. These synthetic images were used to supplement training datasets for pathology classifiers, with the aim of improving their performance. The evaluation involved three datasets (CheXpert, MIMIC-CXR, and Emory Chest X-ray) and various experiments, including supplementing real data with synthetic data, training with purely synthetic data, and mixing synthetic data with external datasets. Performance was assessed using the area under the receiver operating curve (AUROC). Adding synthetic data to real datasets resulted in a notable increase in AUROC values (up to 0.02 in internal and external test sets with 1000% supplementation, p-value less than 0.01 in all instances). When classifiers were trained exclusively on synthetic data, they achieved performance levels comparable to those trained on real data with 200%-300% data supplementation. The combination of real and synthetic data from different sources demonstrated enhanced model generalizability, increasing model AUROC from 0.76 to 0.80 on the internal test set (p-value less than 0.01). In conclusion, synthetic data supplementation significantly improves the performance and generalizability of pathology classifiers in medical imaging.
|
http://arxiv.org/abs/2311.09402v2
|
[
"Bardia Khosravi",
"Frank Li",
"Theo Dapamede",
"Pouria Rouzrokh",
"Cooper U. Gamble",
"Hari M. Trivedi",
"Cody C. Wyles",
"Andrew B. Sellergren",
"Saptarshi Purkayastha",
"Bradley J. Erickson",
"Judy W. Gichoya"
] |
2024-07-08T00:56:36Z
|
2023-11-15T21:58:01Z
|
2406.15786
|
What Matters in Transformers? Not All Attention is Needed
|
Scaling Transformer-based large language models (LLMs) has demonstrated promising performance across various tasks. However, this scaling also introduces redundant structures, posing challenges for real-world deployment. Despite some recognition of redundancy in LLMs, the variability of redundancy across different structures, such as MLP and Attention layers, is under-explored. In this work, we investigate the varying redundancy across different modules within Transformers, including Blocks, MLP, and Attention layers, using a similarity-based metric. This metric operates on the premise that redundant structures produce outputs highly similar to their inputs. Surprisingly, while attention layers are essential for transformers and distinguish them from other mainstream architectures, we found that a large proportion of attention layers exhibit excessively high similarity and can be safely pruned without degrading performance, leading to reduced memory and computation costs. Additionally, we further propose a method that jointly drops Attention and MLP layers, achieving improved performance and dropping ratios. Extensive experiments demonstrate the effectiveness of our methods, e.g., Llama-3-70B maintains comparable performance even after pruning half of the attention layers. Our findings provide valuable insights for future network architecture design. The code will be released at: url{https://github.com/Shwai-He/LLM-Drop}.
|
http://arxiv.org/pdf/2406.15786v2
|
[
"Shwai He",
"Guoheng Sun",
"Zheyu Shen",
"Ang Li"
] |
2024-07-08T00:28:52Z
|
2024-06-22T08:41:48Z
|
2401.11646
|
Nonparametric Density Estimation via Variance-Reduced Sketching
|
Nonparametric density models are of great interest in various scientific and engineering disciplines. Classical density kernel methods, while numerically robust and statistically sound in low-dimensional settings, become inadequate even in moderate higher-dimensional settings due to the curse of dimensionality. In this paper, we introduce a new framework called Variance-Reduced Sketching (VRS), specifically designed to estimate multivariable density functions with a reduced curse of dimensionality. Our framework conceptualizes multivariable functions as infinite-size matrices, and facilitates a new sketching technique motivated by numerical linear algebra literature to reduce the variance in density estimation problems. We demonstrate the robust numerical performance of VRS through a series of simulated experiments and real-world data applications. Notably, VRS shows remarkable improvement over existing neural network estimators and classical kernel methods in numerous density models. Additionally, we offer theoretical justifications for VRS to support its ability to deliver nonparametric density estimation with a reduced curse of dimensionality.
|
http://arxiv.org/pdf/2401.11646v2
|
[
"Yifan Peng",
"Yuehaw Khoo",
"Daren Wang"
] |
2024-07-08T00:27:04Z
|
2024-01-22T01:45:34Z
|
2407.05527
|
Rethinking Image Skip Connections in StyleGAN2
|
Various models based on StyleGAN have gained significant traction in the field of image synthesis, attributed to their robust training stability and superior performances. Within the StyleGAN framework, the adoption of image skip connection is favored over the traditional residual connection. However, this preference is just based on empirical observations; there has not been any in-depth mathematical analysis on it yet. To rectify this situation, this brief aims to elucidate the mathematical meaning of the image skip connection and introduce a groundbreaking methodology, termed the image squeeze connection, which significantly improves the quality of image synthesis. Specifically, we analyze the image skip connection technique to reveal its problem and introduce the proposed method which not only effectively boosts the GAN performance but also reduces the required number of network parameters. Extensive experiments on various datasets demonstrate that the proposed method consistently enhances the performance of state-of-the-art models based on StyleGAN. We believe that our findings represent a vital advancement in the field of image synthesis, suggesting a novel direction for future research and applications.
|
http://arxiv.org/pdf/2407.05527v1
|
[
"Seung Park",
"Yong-Goo Shin"
] |
2024-07-08T00:21:17Z
|
2024-07-08T00:21:17Z
|
2407.05526
|
Can Machines Learn the True Probabilities?
|
When there exists uncertainty, AI machines are designed to make decisions so as to reach the best expected outcomes. Expectations are based on true facts about the objective environment the machines interact with, and those facts can be encoded into AI models in the form of true objective probability functions. Accordingly, AI models involve probabilistic machine learning in which the probabilities should be objectively interpreted. We prove under some basic assumptions when machines can learn the true objective probabilities, if any, and when machines cannot learn them.
|
http://arxiv.org/pdf/2407.05526v1
|
[
"Jinsook Kim"
] |
2024-07-08T00:19:43Z
|
2024-07-08T00:19:43Z
|
2401.10134
|
Spatial-Temporal Large Language Model for Traffic Prediction
|
Traffic prediction, an essential component for intelligent transportation systems, endeavours to use historical data to foresee future traffic features at specific locations. Although existing traffic prediction models often emphasize developing complex neural network structures, their accuracy has not improved. Recently, large language models have shown outstanding capabilities in time series analysis. Differing from existing models, LLMs progress mainly through parameter expansion and extensive pretraining while maintaining their fundamental structures. Motivated by these developments, we propose a Spatial-Temporal Large Language Model (ST-LLM) for traffic prediction. In the ST-LLM, we define timesteps at each location as tokens and design a spatial-temporal embedding to learn the spatial location and global temporal patterns of these tokens. Additionally, we integrate these embeddings by a fusion convolution to each token for a unified spatial-temporal representation. Furthermore, we innovate a partially frozen attention strategy to adapt the LLM to capture global spatial-temporal dependencies for traffic prediction. Comprehensive experiments on real traffic datasets offer evidence that ST-LLM is a powerful spatial-temporal learner that outperforms state-of-the-art models. Notably, the ST-LLM also exhibits robust performance in both few-shot and zero-shot prediction scenarios. The code is publicly available at https://github.com/ChenxiLiu-HNU/ST-LLM.
|
http://arxiv.org/pdf/2401.10134v4
|
[
"Chenxi Liu",
"Sun Yang",
"Qianxiong Xu",
"Zhishuai Li",
"Cheng Long",
"Ziyue Li",
"Rui Zhao"
] |
2024-07-07T23:57:29Z
|
2024-01-18T17:03:59Z
|
2407.05520
|
A Theory of Machine Learning
|
We critically review three major theories of machine learning and provide a new theory according to which machines learn a function when the machines successfully compute it. We show that this theory challenges common assumptions in the statistical and the computational learning theories, for it implies that learning true probabilities is equivalent neither to obtaining a correct calculation of the true probabilities nor to obtaining an almost-sure convergence to them. We also briefly discuss some case studies from natural language processing and macroeconomics from the perspective of the new theory.
|
http://arxiv.org/pdf/2407.05520v1
|
[
"Jinsook Kim",
"Jinho Kang"
] |
2024-07-07T23:57:10Z
|
2024-07-07T23:57:10Z
|
2309.09924
|
Learning graph geometry and topology using dynamical systems based
message-passing
|
In this paper we introduce DYMAG: a message passing paradigm for GNNs built on the expressive power of continuous, multiscale graph-dynamics. Standard discrete-time message passing algorithms implicitly make use of simplistic graph dynamics and aggregation schemes which limit their ability to capture fundamental graph topological properties. By contrast, DYMAG makes use of complex graph dynamics based on the heat and wave equation as well as a more complex equation which admits chaotic solutions. The continuous nature of the dynamics are leveraged to generate multiscale (dynamic-time snapshot) representations which we prove are linked to various graph topological and spectral properties. We demonstrate experimentally that DYMAG achieves superior performance in recovering the generating parameters of Erd"os-Renyi and stochastic block model random graphs and the persistent homology of synthetic graphs and citation network. Since the behavior of proteins and biomolecules is sensitive to graph topology and exhibits important structure at multiple scales, we find that DYMAG outperforms other methods at predicting salient features of various biomolecules.
|
http://arxiv.org/pdf/2309.09924v4
|
[
"Dhananjay Bhaskar",
"Yanlei Zhang",
"Charles Xu",
"Xingzhi Sun",
"Oluwadamilola Fasina",
"Guy Wolf",
"Maximilian Nickel",
"Michael Perlmutter",
"Smita Krishnaswamy"
] |
2024-07-07T23:08:05Z
|
2023-09-18T16:39:51Z
|
2407.05511
|
Provably Efficient Long-Horizon Exploration in Monte Carlo Tree Search
through State Occupancy Regularization
|
Monte Carlo tree search (MCTS) has been successful in a variety of domains, but faces challenges with long-horizon exploration when compared to sampling-based motion planning algorithms like Rapidly-Exploring Random Trees. To address these limitations of MCTS, we derive a tree search algorithm based on policy optimization with state occupancy measure regularization, which we call {it Volume-MCTS}. We show that count-based exploration and sampling-based motion planning can be derived as approximate solutions to this state occupancy measure regularized objective. We test our method on several robot navigation problems, and find that Volume-MCTS outperforms AlphaZero and displays significantly better long-horizon exploration properties.
|
http://arxiv.org/pdf/2407.05511v1
|
[
"Liam Schramm",
"Abdeslam Boularias"
] |
2024-07-07T22:58:52Z
|
2024-07-07T22:58:52Z
|
2407.05510
|
SCATTER: Algorithm-Circuit Co-Sparse Photonic Accelerator with
Thermal-Tolerant, Power-Efficient In-situ Light Redistribution
|
Photonic computing has emerged as a promising solution for accelerating computation-intensive artificial intelligence (AI) workloads. However, limited reconfigurability, high electrical-optical conversion cost, and thermal sensitivity limit the deployment of current optical analog computing engines to support power-restricted, performance-sensitive AI workloads at scale. Sparsity provides a great opportunity for hardware-efficient AI accelerators. However, current dense photonic accelerators fail to fully exploit the power-saving potential of algorithmic sparsity. It requires sparsity-aware hardware specialization with a fundamental re-design of photonic tensor core topology and cross-layer device-circuit-architecture-algorithm co-optimization aware of hardware non-ideality and power bottleneck. To trim down the redundant power consumption while maximizing robustness to thermal variations, we propose SCATTER, a novel algorithm-circuit co-sparse photonic accelerator featuring dynamically reconfigurable signal path via thermal-tolerant, power-efficient in-situ light redistribution and power gating. A power-optimized, crosstalk-aware dynamic sparse training framework is introduced to explore row-column structured sparsity and ensure marginal accuracy loss and maximum power efficiency. The extensive evaluation shows that our cross-stacked optimized accelerator SCATTER achieves a 511X area reduction and 12.4X power saving with superior crosstalk tolerance that enables unprecedented circuit layout compactness and on-chip power efficiency.
|
http://arxiv.org/pdf/2407.05510v1
|
[
"Ziang Yin",
"Nicholas Gangi",
"Meng Zhang",
"Jeff Zhang",
"Rena Huang",
"Jiaqi Gu"
] |
2024-07-07T22:57:44Z
|
2024-07-07T22:57:44Z
|
2402.08847
|
Space-Time Diffusion Bridge
|
In this study, we introduce a novel method for generating new synthetic samples that are independent and identically distributed (i.i.d.) from high-dimensional real-valued probability distributions, as defined implicitly by a set of Ground Truth (GT) samples. Central to our method is the integration of space-time mixing strategies that extend across temporal and spatial dimensions. Our methodology is underpinned by three interrelated stochastic processes designed to enable optimal transport from an easily tractable initial probability distribution to the target distribution represented by the GT samples: (a) linear processes incorporating space-time mixing that yield Gaussian conditional probability densities, (b) their diffusion bridge analogs that are conditioned to the initial and final state vectors, and (c) nonlinear stochastic processes refined through score-matching techniques. The crux of our training regime involves fine-tuning the nonlinear model, and potentially the linear models -- to align closely with the GT data. We validate the efficacy of our space-time diffusion approach with numerical experiments, laying the groundwork for more extensive future theory and experiments to fully authenticate the method, particularly providing a more efficient (possibly simulation-free) inference.
|
http://arxiv.org/pdf/2402.08847v2
|
[
"Hamidreza Behjoo",
"Michael Chertkov"
] |
2024-07-07T22:44:32Z
|
2024-02-13T23:26:11Z
|
2304.00776
|
Chain-of-Thought Predictive Control
|
We study generalizable policy learning from demonstrations for complex low-level control (e.g., contact-rich object manipulations). We propose a novel hierarchical imitation learning method that utilizes sub-optimal demos. Firstly, we propose an observation space-agnostic approach that efficiently discovers the multi-step subskill decomposition of the demos in an unsupervised manner. By grouping temporarily close and functionally similar actions into subskill-level demo segments, the observations at the segment boundaries constitute a chain of planning steps for the task, which we refer to as the chain-of-thought (CoT). Next, we propose a Transformer-based design that effectively learns to predict the CoT as the subskill-level guidance. We couple action and subskill predictions via learnable prompt tokens and a hybrid masking strategy, which enable dynamically updated guidance at test time and improve feature representation of the trajectory for generalizable policy learning. Our method, Chain-of-Thought Predictive Control (CoTPC), consistently surpasses existing strong baselines on challenging manipulation tasks with sub-optimal demos.
|
http://arxiv.org/pdf/2304.00776v2
|
[
"Zhiwei Jia",
"Vineet Thumuluri",
"Fangchen Liu",
"Linghao Chen",
"Zhiao Huang",
"Hao Su"
] |
2024-07-07T22:06:34Z
|
2023-04-03T07:59:13Z
|
2208.13065
|
Towards Improving Unit Commitment Economics: An Add-On Tailor for
Renewable Energy and Reserve Predictions
|
Generally, day-ahead unit commitment (UC) is conducted in a predict-then-optimize process: it starts by predicting the renewable energy source (RES) availability and system reserve requirements; given the predictions, the UC model is then optimized to determine the economic operation plans. In fact, predictions within the process are raw. In other words, if the predictions are further tailored to assist UC in making the economic operation plans against realizations of the RES and reserve requirements, UC economics will benefit significantly. To this end, this paper presents a cost-oriented tailor of RES-and-reserve predictions for UC, deployed as an add-on to the predict-then-optimize process. The RES-and-reserve tailor is trained by solving a bi-level mixed-integer programming model: the upper level trains the tailor based on its induced operating cost; the lower level, given tailored predictions, mimics the system operation process and feeds the induced operating cost back to the upper level; finally, the upper level evaluates the training quality according to the fed-back cost. Through this training, the tailor learns to customize the raw predictions into cost-oriented predictions. Moreover, the tailor can be embedded into the existing predict-then-optimize process as an add-on, improving the UC economics. Lastly, the presented method is compared to traditional, binary-relaxation, neural network-based, stochastic, and robust methods.
|
http://arxiv.org/pdf/2208.13065v4
|
[
"Xianbang Chen",
"Yikui Liu",
"Lei Wu"
] |
2024-07-07T22:00:33Z
|
2022-08-27T18:03:05Z
|
2311.15161
|
Hessian Aware Low-Rank Perturbation for Order-Robust Continual Learning
|
Continual learning aims to learn a series of tasks sequentially without forgetting the knowledge acquired from the previous ones. In this work, we propose the Hessian Aware Low-Rank Perturbation algorithm for continual learning. By modeling the parameter transitions along the sequential tasks with the weight matrix transformation, we propose to apply the low-rank approximation on the task-adaptive parameters in each layer of the neural networks. Specifically, we theoretically demonstrate the quantitative relationship between the Hessian and the proposed low-rank approximation. The approximation ranks are then globally determined according to the marginal increment of the empirical loss estimated by the layer-specific gradient and low-rank approximation error. Furthermore, we control the model capacity by pruning less important parameters to diminish the parameter growth. We conduct extensive experiments on various benchmarks, including a dataset with large-scale tasks, and compare our method against some recent state-of-the-art methods to demonstrate the effectiveness and scalability of our proposed method. Empirical results show that our method performs better on different benchmarks, especially in achieving task order robustness and handling the forgetting issue. The source code is at https://github.com/lijiaqi/HALRP.
|
http://arxiv.org/abs/2311.15161v4
|
[
"Jiaqi Li",
"Yuanhao Lai",
"Rui Wang",
"Changjian Shui",
"Sabyasachi Sahoo",
"Charles X. Ling",
"Shichun Yang",
"Boyu Wang",
"Christian Gagné",
"Fan Zhou"
] |
2024-07-07T21:11:23Z
|
2023-11-26T01:44:01Z
|
2407.05487
|
Multi-level Reliability Interface for Semantic Communications over
Wireless Networks
|
Semantic communication, when examined through the lens of joint source-channel coding (JSCC), maps source messages directly into channel input symbols, where the measure of success is defined by end-to-end distortion rather than traditional metrics such as block error rate. Previous studies have shown significant improvements achieved through deep learning (DL)-driven JSCC compared to traditional separate source and channel coding. However, JSCC is impractical in existing communication networks, where application and network providers are typically different entities connected over general-purpose TCP/IP links. In this paper, we propose designing the source and channel mappings separately and sequentially via a novel multi-level reliability interface. This conceptual interface enables semi-JSCC at both the learned source and channel mappers and achieves many of the gains observed in existing DL-based JSCC work (which would require a fully joint design between the application and the network), such as lower end-to-end distortion and graceful degradation of distortion with channel quality. We believe this work represents an important step towards realizing semantic communications in wireless networks.
|
http://arxiv.org/pdf/2407.05487v1
|
[
"Tze-Yang Tung",
"Homa Esfahanizadeh",
"Jinfeng Du",
"Harish Viswanathan"
] |
2024-07-07T20:15:10Z
|
2024-07-07T20:15:10Z
|
2407.05484
|
Learning to Price Homogeneous Data
|
We study a data pricing problem, where a seller has access to $N$ homogeneous data points (e.g. drawn i.i.d. from some distribution). There are $m$ types of buyers in the market, where buyers of the same type $i$ have the same valuation curve $v_i:[N]rightarrow [0,1]$, where $v_i(n)$ is the value for having $n$ data points. textit{A priori}, the seller is unaware of the distribution of buyers, but can repeat the market for $T$ rounds so as to learn the revenue-optimal pricing curve $p:[N] rightarrow [0, 1]$. To solve this online learning problem, we first develop novel discretization schemes to approximate any pricing curve. When compared to prior work, the size of our discretization schemes scales gracefully with the approximation parameter, which translates to better regret in online learning. Under assumptions like smoothness and diminishing returns which are satisfied by data, the discretization size can be reduced further. We then turn to the online learning problem, both in the stochastic and adversarial settings. On each round, the seller chooses an emph{anonymous} pricing curve $p_t$. A new buyer appears and may choose to purchase some amount of data. She then reveals her type emph{only if} she makes a purchase. Our online algorithms build on classical algorithms such as UCB and FTPL, but require novel ideas to account for the asymmetric nature of this feedback and to deal with the vastness of the space of pricing curves. Using the improved discretization schemes previously developed, we are able to achieve $tilde{O}left(msqrt{T}right)$ regret in the stochastic setting and $tilde{O}left(m^{frac{3}{2}}sqrt{T}right)$ regret in the adversarial setting.
|
http://arxiv.org/pdf/2407.05484v1
|
[
"Keran Chen",
"Joon Suk Huh",
"Kirthevasan Kandasamy"
] |
2024-07-07T20:02:52Z
|
2024-07-07T20:02:52Z
|
2407.05483
|
Just read twice: closing the recall gap for recurrent language models
|
Recurrent large language models that compete with Transformers in language modeling perplexity are emerging at a rapid rate (e.g., Mamba, RWKV). Excitingly, these architectures use a constant amount of memory during inference. However, due to the limited memory, recurrent LMs cannot recall and use all the information in long contexts leading to brittle in-context learning (ICL) quality. A key challenge for efficient LMs is selecting what information to store versus discard. In this work, we observe the order in which information is shown to the LM impacts the selection difficulty. To formalize this, we show that the hardness of information recall reduces to the hardness of a problem called set disjointness (SD), a quintessential problem in communication complexity that requires a streaming algorithm (e.g., recurrent model) to decide whether inputted sets are disjoint. We empirically and theoretically show that the recurrent memory required to solve SD changes with set order, i.e., whether the smaller set appears first in-context. Our analysis suggests, to mitigate the reliance on data order, we can put information in the right order in-context or process prompts non-causally. Towards that end, we propose: (1) JRT-Prompt, where context gets repeated multiple times in the prompt, effectively showing the model all data orders. This gives $11.0 pm 1.3$ points of improvement, averaged across $16$ recurrent LMs and the $6$ ICL tasks, with $11.9times$ higher throughput than FlashAttention-2 for generation prefill (length $32$k, batch size $16$, NVidia H100). We then propose (2) JRT-RNN, which uses non-causal prefix-linear-attention to process prompts and provides $99%$ of Transformer quality at $360$M params., $30$B tokens and $96%$ at $1.3$B params., $50$B tokens on average across the tasks, with $19.2times$ higher throughput for prefill than FA2.
|
http://arxiv.org/pdf/2407.05483v1
|
[
"Simran Arora",
"Aman Timalsina",
"Aaryan Singhal",
"Benjamin Spector",
"Sabri Eyuboglu",
"Xinyi Zhao",
"Ashish Rao",
"Atri Rudra",
"Christopher Ré"
] |
2024-07-07T19:55:09Z
|
2024-07-07T19:55:09Z
|
2407.06237
|
Discounted Pseudocosts in MILP
|
In this article, we introduce the concept of discounted pseudocosts, inspired by discounted total reward in reinforcement learning, and explore their application in mixed-integer linear programming (MILP). Traditional pseudocosts estimate changes in the objective function due to variable bound changes during the branch-and-bound process. By integrating reinforcement learning concepts, we propose a novel approach incorporating a forward-looking perspective into pseudocost estimation. We present the motivation behind discounted pseudocosts and discuss how they represent the anticipated reward for branching after one level of exploration in the MILP problem space. Initial experiments on MIPLIB 2017 benchmark instances demonstrate the potential of discounted pseudocosts to enhance branching strategies and accelerate the solution process for challenging MILP problems.
|
http://arxiv.org/pdf/2407.06237v1
|
[
"Krunal Kishor Patel"
] |
2024-07-07T19:41:38Z
|
2024-07-07T19:41:38Z
|
2403.15941
|
Explore until Confident: Efficient Exploration for Embodied Question
Answering
|
We consider the problem of Embodied Question Answering (EQA), which refers to settings where an embodied agent such as a robot needs to actively explore an environment to gather information until it is confident about the answer to a question. In this work, we leverage the strong semantic reasoning capabilities of large vision-language models (VLMs) to efficiently explore and answer such questions. However, there are two main challenges when using VLMs in EQA: they do not have an internal memory for mapping the scene to be able to plan how to explore over time, and their confidence can be miscalibrated and can cause the robot to prematurely stop exploration or over-explore. We propose a method that first builds a semantic map of the scene based on depth information and via visual prompting of a VLM - leveraging its vast knowledge of relevant regions of the scene for exploration. Next, we use conformal prediction to calibrate the VLM's question answering confidence, allowing the robot to know when to stop exploration - leading to a more calibrated and efficient exploration strategy. To test our framework in simulation, we also contribute a new EQA dataset with diverse, realistic human-robot scenarios and scenes built upon the Habitat-Matterport 3D Research Dataset (HM3D). Both simulated and real robot experiments show our proposed approach improves the performance and efficiency over baselines that do no leverage VLM for exploration or do not calibrate its confidence. Webpage with experiment videos and code: https://explore-eqa.github.io/
|
http://arxiv.org/pdf/2403.15941v3
|
[
"Allen Z. Ren",
"Jaden Clark",
"Anushri Dixit",
"Masha Itkina",
"Anirudha Majumdar",
"Dorsa Sadigh"
] |
2024-07-07T19:40:31Z
|
2024-03-23T22:04:03Z
|
2404.11509
|
VC Theory for Inventory Policies
|
Advances in computational power and AI have increased interest in reinforcement learning approaches to inventory management. This paper provides a theoretical foundation for these approaches and investigates the benefits of restricting to policy structures that are well-established by inventory theory. In particular, we prove generalization guarantees for learning several well-known classes of inventory policies, including base-stock and (s, S) policies, by leveraging the celebrated Vapnik-Chervonenkis (VC) theory. We apply the Pseudo-dimension and Fat-shattering dimension from VC theory to determine the generalization error of inventory policies, that is, the difference between an inventory policy's performance on training data and its expected performance on unseen data. We focus on a classical setting without contexts, but allow for an arbitrary distribution over demand sequences and do not make any assumptions such as independence over time. We corroborate our supervised learning results using numerical simulations. Managerially, our theory and simulations translate to the following insights. First, there is a principle of ``learning less is more'' in inventory management: depending on the amount of data available, it may be beneficial to restrict oneself to a simpler, albeit suboptimal, class of inventory policies to minimize overfitting errors. Second, the number of parameters in a policy class may not be the correct measure of overfitting error: in fact, the class of policies defined by T time-varying base-stock levels exhibits a generalization error an order of magnitude lower than that of the two-parameter (s, S) policy class. Finally, our research suggests situations in which it could be beneficial to incorporate the concepts of base-stock and inventory position into black-box learning machines, instead of having these machines directly learn the order quantity actions.
|
http://arxiv.org/pdf/2404.11509v2
|
[
"Yaqi Xie",
"Will Ma",
"Linwei Xin"
] |
2024-07-07T19:32:17Z
|
2024-04-17T16:05:03Z
|
2402.05133
|
Personalized Language Modeling from Personalized Human Feedback
|
Reinforcement Learning from Human Feedback (RLHF) is commonly used to fine-tune large language models to better align with human preferences. However, the underlying premise of algorithms developed under this framework can be problematic when user preferences encoded in human feedback are diverse. In this work, we aim to address this problem by developing methods for building personalized language models. We first formally introduce the task of learning from personalized human feedback and explain why vanilla RLHF can be ineffective in this context. We then propose a general Personalized-RLHF (P-RLHF) framework, including a user model that maps user information to user representations and can flexibly encode our assumptions on user preferences. We develop new learning objectives to perform personalized Direct Preference Optimization that jointly learns a user model and a personalized language model. We demonstrate the efficacy of our proposed method through (1) a synthetic task where we fine-tune a GPT-J 6B model to align with users with conflicting preferences on generation length; and (2) an instruction following task where we fine-tune a Tulu-7B model to generate responses for users with diverse preferences on the style of responses. In both cases, our learned models can generate personalized responses that are better aligned with the preferences of individual users.
|
http://arxiv.org/pdf/2402.05133v2
|
[
"Xinyu Li",
"Zachary C. Lipton",
"Liu Leqi"
] |
2024-07-07T19:31:21Z
|
2024-02-06T04:18:58Z
|
2403.05798
|
$\textbf{S}^2$IP-LLM: Semantic Space Informed Prompt Learning with LLM
for Time Series Forecasting
|
Recently, there has been a growing interest in leveraging pre-trained large language models (LLMs) for various time series applications. However, the semantic space of LLMs, established through the pre-training, is still underexplored and may help yield more distinctive and informative representations to facilitate time series forecasting. To this end, we propose Semantic Space Informed Prompt learning with LLM ($S^2$IP-LLM) to align the pre-trained semantic space with time series embeddings space and perform time series forecasting based on learned prompts from the joint space. We first design a tokenization module tailored for cross-modality alignment, which explicitly concatenates patches of decomposed time series components to create embeddings that effectively encode the temporal dynamics. Next, we leverage the pre-trained word token embeddings to derive semantic anchors and align selected anchors with time series embeddings by maximizing the cosine similarity in the joint space. This way, $S^2$IP-LLM can retrieve relevant semantic anchors as prompts to provide strong indicators (context) for time series that exhibit different temporal dynamics. With thorough empirical studies on multiple benchmark datasets, we demonstrate that the proposed $S^2$IP-LLM can achieve superior forecasting performance over state-of-the-art baselines. Furthermore, our ablation studies and visualizations verify the necessity of prompt learning informed by semantic space.
|
http://arxiv.org/pdf/2403.05798v2
|
[
"Zijie Pan",
"Yushan Jiang",
"Sahil Garg",
"Anderson Schneider",
"Yuriy Nevmyvaka",
"Dongjin Song"
] |
2024-07-07T19:14:34Z
|
2024-03-09T05:20:48Z
|
2405.14108
|
Deep Learning for Protein-Ligand Docking: Are We There Yet?
|
The effects of ligand binding on protein structures and their in vivo functions carry numerous implications for modern biomedical research and biotechnology development efforts such as drug discovery. Although several deep learning (DL) methods and benchmarks designed for protein-ligand docking have recently been introduced, to date no prior works have systematically studied the behavior of docking methods within the practical context of (1) using predicted (apo) protein structures for docking (e.g., for broad applicability); (2) docking multiple ligands concurrently to a given target protein (e.g., for enzyme design); and (3) having no prior knowledge of binding pockets (e.g., for pocket generalization). To enable a deeper understanding of docking methods' real-world utility, we introduce PoseBench, the first comprehensive benchmark for practical protein-ligand docking. PoseBench enables researchers to rigorously and systematically evaluate DL docking methods for apo-to-holo protein-ligand docking and protein-ligand structure generation using both single and multi-ligand benchmark datasets, the latter of which we introduce for the first time to the DL community. Empirically, using PoseBench, we find that all recent DL docking methods but one fail to generalize to multi-ligand protein targets and also that template-based docking algorithms perform equally well or better for multi-ligand docking as recent single-ligand DL docking methods, suggesting areas of improvement for future work. Code, data, tutorials, and benchmark results are available at https://github.com/BioinfoMachineLearning/PoseBench.
|
http://arxiv.org/pdf/2405.14108v3
|
[
"Alex Morehead",
"Nabin Giri",
"Jian Liu",
"Jianlin Cheng"
] |
2024-07-07T19:12:04Z
|
2024-05-23T02:27:39Z
|
2305.07644
|
Beware of diffusion models for synthesizing medical images -- A
comparison with GANs in terms of memorizing brain MRI and chest x-ray images
|
Diffusion models were initially developed for text-to-image generation and are now being utilized to generate high quality synthetic images. Preceded by GANs, diffusion models have shown impressive results using various evaluation metrics. However, commonly used metrics such as FID and IS are not suitable for determining whether diffusion models are simply reproducing the training images. Here we train StyleGAN and a diffusion model, using BRATS20, BRATS21 and a chest x-ray pneumonia dataset, to synthesize brain MRI and chest x-ray images, and measure the correlation between the synthetic images and all training images. Our results show that diffusion models are more likely to memorize the training images, compared to StyleGAN, especially for small datasets and when using 2D slices from 3D volumes. Researchers should be careful when using diffusion models (and to some extent GANs) for medical imaging, if the final goal is to share the synthetic images.
|
http://arxiv.org/pdf/2305.07644v3
|
[
"Muhammad Usman Akbar",
"Wuhao Wang",
"Anders Eklund"
] |
2024-07-07T19:09:39Z
|
2023-05-12T17:55:40Z
|
2403.04750
|
JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics Framework
|
Particle-based fluid simulations have emerged as a powerful tool for solving the Navier-Stokes equations, especially in cases that include intricate physics and free surfaces. The recent addition of machine learning methods to the toolbox for solving such problems is pushing the boundary of the quality vs. speed tradeoff of such numerical simulations. In this work, we lead the way to Lagrangian fluid simulators compatible with deep learning frameworks, and propose JAX-SPH - a Smoothed Particle Hydrodynamics (SPH) framework implemented in JAX. JAX-SPH builds on the code for dataset generation from the LagrangeBench project (Toshev et al., 2023) and extends this code in multiple ways: (a) integration of further key SPH algorithms, (b) restructuring the code toward a Python package, (c) verification of the gradients through the solver, and (d) demonstration of the utility of the gradients for solving inverse problems as well as a Solver-in-the-Loop application. Our code is available at https://github.com/tumaer/jax-sph.
|
http://arxiv.org/pdf/2403.04750v2
|
[
"Artur P. Toshev",
"Harish Ramachandran",
"Jonas A. Erbesdobler",
"Gianluca Galletti",
"Johannes Brandstetter",
"Nikolaus A. Adams"
] |
2024-07-07T17:53:28Z
|
2024-03-07T18:53:53Z
|
2402.06275
|
Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamics
|
Smoothed particle hydrodynamics (SPH) is omnipresent in modern engineering and scientific disciplines. SPH is a class of Lagrangian schemes that discretize fluid dynamics via finite material points that are tracked through the evolving velocity field. Due to the particle-like nature of the simulation, graph neural networks (GNNs) have emerged as appealing and successful surrogates. However, the practical utility of such GNN-based simulators relies on their ability to faithfully model physics, providing accurate and stable predictions over long time horizons - which is a notoriously hard problem. In this work, we identify particle clustering originating from tensile instabilities as one of the primary pitfalls. Based on these insights, we enhance both training and rollout inference of state-of-the-art GNN-based simulators with varying components from standard SPH solvers, including pressure, viscous, and external force components. All Neural SPH-enhanced simulators achieve better performance than the baseline GNNs, often by orders of magnitude in terms of rollout error, allowing for significantly longer rollouts and significantly better physics modeling. Code available at https://github.com/tumaer/neuralsph.
|
http://arxiv.org/pdf/2402.06275v2
|
[
"Artur P. Toshev",
"Jonas A. Erbesdobler",
"Nikolaus A. Adams",
"Johannes Brandstetter"
] |
2024-07-07T17:44:40Z
|
2024-02-09T09:40:12Z
|
2305.19685
|
Deep Stochastic Mechanics
|
This paper introduces a novel deep-learning-based approach for numerical simulation of a time-evolving Schr"odinger equation inspired by stochastic mechanics and generative diffusion models. Unlike existing approaches, which exhibit computational complexity that scales exponentially in the problem dimension, our method allows us to adapt to the latent low-dimensional structure of the wave function by sampling from the Markovian diffusion. Depending on the latent dimension, our method may have far lower computational complexity in higher dimensions. Moreover, we propose novel equations for stochastic quantum mechanics, resulting in quadratic computational complexity with respect to the number of dimensions. Numerical simulations verify our theoretical findings and show a significant advantage of our method compared to other deep-learning-based approaches used for quantum mechanics.
|
http://arxiv.org/pdf/2305.19685v5
|
[
"Elena Orlova",
"Aleksei Ustimenko",
"Ruoxi Jiang",
"Peter Y. Lu",
"Rebecca Willett"
] |
2024-07-07T17:42:40Z
|
2023-05-31T09:28:03Z
|
2407.02362
|
Fast, Scalable, Energy-Efficient Non-element-wise Matrix Multiplication
on FPGA
|
Modern Neural Network (NN) architectures heavily rely on vast numbers of multiply-accumulate arithmetic operations, constituting the predominant computational cost. Therefore, this paper proposes a high-throughput, scalable and energy efficient non-element-wise matrix multiplication unit on FPGAs as a basic component of the NNs. We firstly streamline inter-layer and intra-layer redundancies of MADDNESS algorithm, a LUT-based approximate matrix multiplication, to design a fast, efficient scalable approximate matrix multiplication module termed "Approximate Multiplication Unit (AMU)". The AMU optimizes LUT-based matrix multiplications further through dedicated memory management and access design, decoupling computational overhead from input resolution and boosting FPGA-based NN accelerator efficiency significantly. The experimental results show that using our AMU achieves up to 9x higher throughput and 112x higher energy efficiency over the state-of-the-art solutions for the FPGA-based Quantised Neural Network (QNN) accelerators.
|
http://arxiv.org/pdf/2407.02362v2
|
[
"Xuqi Zhu",
"Huaizhi Zhang",
"JunKyu Lee",
"Jiacheng Zhu",
"Chandrajit Pal",
"Sangeet Saha",
"Klaus D. McDonald-Maier",
"Xiaojun Zhai"
] |
2024-07-07T17:20:51Z
|
2024-07-02T15:28:10Z
|
2407.02423
|
On the Anatomy of Attention
|
We introduce a category-theoretic diagrammatic formalism in order to systematically relate and reason about machine learning models. Our diagrams present architectures intuitively but without loss of essential detail, where natural relationships between models are captured by graphical transformations, and important differences and similarities can be identified at a glance. In this paper, we focus on attention mechanisms: translating folklore into mathematical derivations, and constructing a taxonomy of attention variants in the literature. As a first example of an empirical investigation underpinned by our formalism, we identify recurring anatomical components of attention, which we exhaustively recombine to explore a space of variations on the attention mechanism.
|
http://arxiv.org/pdf/2407.02423v2
|
[
"Nikhil Khatri",
"Tuomas Laakkonen",
"Jonathon Liu",
"Vincent Wang-Maścianica"
] |
2024-07-07T17:03:05Z
|
2024-07-02T16:50:26Z
|
2405.13235
|
Geometric Transformation Uncertainty for Improving 3D Fetal Brain Pose
Prediction from Freehand 2D Ultrasound Videos
|
Accurately localizing two-dimensional (2D) ultrasound (US) fetal brain images in the 3D brain, using minimal computational resources, is an important task for automated US analysis of fetal growth and development. We propose an uncertainty-aware deep learning model for automated 3D plane localization in 2D fetal brain images. Specifically, a multi-head network is trained to jointly regress 3D plane pose from 2D images in terms of different geometric transformations. The model explicitly learns to predict uncertainty to allocate higher weight to inputs with low variances across different transformations to improve performance. Our proposed method, QAERTS, demonstrates superior pose estimation accuracy than the state-of-the-art and most of the uncertainty-based approaches, leading to 9% improvement on plane angle (PA) for localization accuracy, and 8% on normalized cross-correlation (NCC) for sampled image quality. QAERTS also demonstrates efficiency, containing 5$times$ fewer parameters than ensemble-based approach, making it advantageous in resource-constrained settings. In addition, QAERTS proves to be more robust to noise effects observed in freehand US scanning by leveraging rotational discontinuities and explicit output uncertainties.
|
http://arxiv.org/pdf/2405.13235v2
|
[
"Jayroop Ramesh",
"Nicola K Dinsdale",
"the INTERGROWTH-21st Consortium",
"Pak-Hei Yeung",
"Ana IL Namburete"
] |
2024-07-07T16:54:09Z
|
2024-05-21T22:42:08Z
|
2401.16468
|
InstructIR: High-Quality Image Restoration Following Human Instructions
|
Image restoration is a fundamental problem that involves recovering a high-quality clean image from its degraded observation. All-In-One image restoration models can effectively restore images from various types and levels of degradation using degradation-specific information as prompts to guide the restoration model. In this work, we present the first approach that uses human-written instructions to guide the image restoration model. Given natural language prompts, our model can recover high-quality images from their degraded counterparts, considering multiple degradation types. Our method, InstructIR, achieves state-of-the-art results on several restoration tasks including image denoising, deraining, deblurring, dehazing, and (low-light) image enhancement. InstructIR improves +1dB over previous all-in-one restoration methods. Moreover, our dataset and results represent a novel benchmark for new research on text-guided image restoration and enhancement. Our code, datasets and models are available at: https://github.com/mv-lab/InstructIR
|
http://arxiv.org/pdf/2401.16468v4
|
[
"Marcos V. Conde",
"Gregor Geigle",
"Radu Timofte"
] |
2024-07-07T16:34:45Z
|
2024-01-29T18:53:33Z
|
2308.04616
|
Machine Learning, Deep Learning and Data Preprocessing Techniques for
Detection, Prediction, and Monitoring of Stress and Stress-related Mental
Disorders: A Scoping Review
|
Background: Mental stress and its consequent mental disorders (MDs) are significant public health issues. With the advent of machine learning (ML), there's potential to harness computational techniques for better understanding and addressing these problems. This review seeks to elucidate the current ML methodologies employed in this domain to enhance the detection, prediction, and analysis of mental stress and MDs. Objective: This review aims to investigate the scope of ML methodologies used in the detection, prediction, and analysis of mental stress and MDs. Methods: Utilizing a rigorous scoping review process with PRISMA-ScR guidelines, this investigation delves into the latest ML algorithms, preprocessing techniques, and data types used in the context of stress and stress-related MDs. Results and Discussion: A total of 98 peer-reviewed publications were examined. The findings highlight that Support Vector Machine (SVM), Neural Network (NN), and Random Forest (RF) models consistently exhibit superior accuracy and robustness among ML algorithms. Physiological parameters such as heart rate measurements and skin response are prevalently used as stress predictors due to their rich explanatory information and ease of data acquisition. Dimensionality reduction techniques, including mappings, feature selection, filtering, and noise reduction, are frequently observed as crucial steps preceding the training of ML algorithms. Conclusion: This review identifies significant research gaps and outlines future directions for the field. These include model interpretability, model personalization, the incorporation of naturalistic settings, and real-time processing capabilities for the detection and prediction of stress and stress-related MDs. Keywords: Machine Learning; Deep Learning; Data Preprocessing; Stress Detection; Stress Prediction; Stress Monitoring; Mental Disorders
|
http://arxiv.org/pdf/2308.04616v2
|
[
"Moein Razavi",
"Samira Ziyadidegan",
"Reza Jahromi",
"Saber Kazeminasab",
"Vahid Janfaza",
"Ahmadreza Mahmoudzadeh",
"Elaheh Baharlouei",
"Farzan Sasangohar"
] |
2024-07-07T16:31:46Z
|
2023-08-08T22:47:12Z
|
2407.05417
|
See Further for Parameter Efficient Fine-tuning by Standing on the
Shoulders of Decomposition
|
The rapid expansion of large foundation models within the pre-training and fine-tuning framework has underscored that larger models often yield better results. However, the scaling up of large foundation models has led to soaring costs in fine-tuning and parameter storage, rendering extensive adaptations impractical. This challenge has sparked the development of parameter-efficient fine-tuning (PEFT), which focuses on optimizing a select subset of parameters while keeping the rest fixed, significantly lowering computational and storage overheads. While recent years have witnessed a significant success in PEFT, a deep understanding of the fundamental principles behind these methods remains unexplored. To this end, here we take the first step to unify all approaches by dissecting them from a decomposition perspective. We initiate a comprehensive mathematical analysis of these methods, allowing us to delve deeply into their underlying mechanisms, and we explore the reasons behind the variations in performance among different techniques. Furthermore, inspired by our theoretical analysis, we introduce two novel PEFT methods alongside a simple yet effective framework designed to enhance the performance of PEFT techniques across various applications. Our empirical validations, conducted across multiple datasets, demonstrate the efficacy of these methods, showcasing both theoretical validity and practical performance improvements under the guidance of our analytical findings. We believe our work will deepen researchers' understanding of PEFT and other techniques, prompting further contemplation and advancing the research across the whole community.
|
http://arxiv.org/pdf/2407.05417v1
|
[
"Chongjie Si",
"Xiaokang Yang",
"Wei Shen"
] |
2024-07-07T15:44:42Z
|
2024-07-07T15:44:42Z
|
2208.13080
|
Information FOMO: The unhealthy fear of missing out on information. A
method for removing misleading data for healthier models
|
Misleading or unnecessary data can have out-sized impacts on the health or accuracy of Machine Learning (ML) models. We present a Bayesian sequential selection method, akin to Bayesian experimental design, that identifies critically important information within a dataset, while ignoring data that is either misleading or brings unnecessary complexity to the surrogate model of choice. Our method improves sample-wise error convergence and eliminates instances where more data leads to worse performance and instabilities of the surrogate model, often termed sample-wise ``double descent''. We find these instabilities are a result of the complexity of the underlying map and linked to extreme events and heavy tails. Our approach has two key features. First, the selection algorithm dynamically couples the chosen model and data. Data is chosen based on its merits towards improving the selected model, rather than being compared strictly against other data. Second, a natural convergence of the method removes the need for dividing the data into training, testing, and validation sets. Instead, the selection metric inherently assesses testing and validation error through global statistics of the model. This ensures that key information is never wasted in testing or validation. The method is applied using both Gaussian process regression and deep neural network surrogate models.
|
http://arxiv.org/pdf/2208.13080v3
|
[
"Ethan Pickering",
"Themistoklis P. Sapsis"
] |
2024-07-07T15:44:26Z
|
2022-08-27T19:43:53Z
|
2407.05410
|
Synthetic Test Data Generation Using Recurrent Neural Networks: A
Position Paper
|
Testing in production-like test environments is an essential part of quality assurance processes in many industries. Provisioning of such test environments, for information-intensive services, involves setting up databases that are rich-enough to enable simulating a wide variety of user scenarios. While production data is perhaps the gold-standard here, many organizations, particularly within the public sectors, are not allowed to use production data for testing purposes due to privacy concerns. The alternatives are to use anonymized data, or synthetically generated data. In this paper, we elaborate on these alternatives and compare them in an industrial context. Further we focus on synthetic data generation and investigate the use of recurrent neural networks for this purpose. In our preliminary experiments, we were able to generate representative and highly accurate data using a recurrent neural network. These results open new research questions that we discuss here, and plan to investigate in our future research.
|
http://arxiv.org/abs/2407.05410v1
|
[
"Razieh Behjati",
"Erik Arisholm",
"Chao Tan",
"Margrethe M. Bedregal"
] |
2024-07-07T15:28:41Z
|
2024-07-07T15:28:41Z
|
2302.08618
|
SplitOut: Out-of-the-Box Training-Hijacking Detection in Split Learning
via Outlier Detection
|
Split learning enables efficient and privacy-aware training of a deep neural network by splitting a neural network so that the clients (data holders) compute the first layers and only share the intermediate output with the central compute-heavy server. This paradigm introduces a new attack medium in which the server has full control over what the client models learn, which has already been exploited to infer the private data of clients and to implement backdoors in the client models. Although previous work has shown that clients can successfully detect such training-hijacking attacks, the proposed methods rely on heuristics, require tuning of many hyperparameters, and do not fully utilize the clients' capabilities. In this work, we show that given modest assumptions regarding the clients' compute capabilities, an out-of-the-box outlier detection method can be used to detect existing training-hijacking attacks with almost-zero false positive rates. We conclude through experiments on different tasks that the simplicity of our approach we name textit{SplitOut} makes it a more viable and reliable alternative compared to the earlier detection methods.
|
http://arxiv.org/pdf/2302.08618v3
|
[
"Ege Erdogan",
"Unat Teksen",
"Mehmet Salih Celiktenyildiz",
"Alptekin Kupcu",
"A. Ercument Cicek"
] |
2024-07-07T15:25:37Z
|
2023-02-16T23:02:39Z
|
2407.00115
|
Instance Temperature Knowledge Distillation
|
Knowledge distillation (KD) enhances the performance of a student network by allowing it to learn the knowledge transferred from a teacher network incrementally. Existing methods dynamically adjust the temperature to enable the student network to adapt to the varying learning difficulties at different learning stages of KD. KD is a continuous process, but when adjusting the temperature, these methods consider only the immediate benefits of the operation in the current learning phase and fail to take into account its future returns. To address this issue, we formulate the adjustment of temperature as a sequential decision-making task and propose a method based on reinforcement learning, termed RLKD. Importantly, we design a novel state representation to enable the agent to make more informed action (i.e. instance temperature adjustment). To handle the problem of delayed rewards in our method due to the KD setting, we explore an instance reward calibration approach. In addition,we devise an efficient exploration strategy that enables the agent to learn valuable instance temperature adjustment policy more efficiently. Our framework can serve as a plug-and-play technique to be inserted into various KD methods easily, and we validate its effectiveness on both image classification and object detection tasks. Our project is at https://www.zayx.me/ITKD.github.io/.
|
http://arxiv.org/pdf/2407.00115v3
|
[
"Zhengbo Zhang",
"Yuxi Zhou",
"Jia Gong",
"Jun Liu",
"Zhigang Tu"
] |
2024-07-07T15:25:05Z
|
2024-06-27T14:00:05Z
|
2407.05404
|
iSign: A Benchmark for Indian Sign Language Processing
|
Indian Sign Language has limited resources for developing machine learning and data-driven approaches for automated language processing. Though text/audio-based language processing techniques have shown colossal research interest and tremendous improvements in the last few years, Sign Languages still need to catch up due to the need for more resources. To bridge this gap, in this work, we propose iSign: a benchmark for Indian Sign Language (ISL) Processing. We make three primary contributions to this work. First, we release one of the largest ISL-English datasets with more than 118K video-sentence/phrase pairs. To the best of our knowledge, it is the largest sign language dataset available for ISL. Second, we propose multiple NLP-specific tasks (including SignVideo2Text, SignPose2Text, Text2Pose, Word Prediction, and Sign Semantics) and benchmark them with the baseline models for easier access to the research community. Third, we provide detailed insights into the proposed benchmarks with a few linguistic insights into the workings of ISL. We streamline the evaluation of Sign Language processing, addressing the gaps in the NLP research community for Sign Languages. We release the dataset, tasks, and models via the following website: https://exploration-lab.github.io/iSign/
|
http://arxiv.org/pdf/2407.05404v1
|
[
"Abhinav Joshi",
"Romit Mohanty",
"Mounika Kanakanti",
"Andesha Mangla",
"Sudeep Choudhary",
"Monali Barbate",
"Ashutosh Modi"
] |
2024-07-07T15:07:35Z
|
2024-07-07T15:07:35Z
|
2407.05399
|
IL-TUR: Benchmark for Indian Legal Text Understanding and Reasoning
|
Legal systems worldwide are inundated with exponential growth in cases and documents. There is an imminent need to develop NLP and ML techniques for automatically processing and understanding legal documents to streamline the legal system. However, evaluating and comparing various NLP models designed specifically for the legal domain is challenging. This paper addresses this challenge by proposing IL-TUR: Benchmark for Indian Legal Text Understanding and Reasoning. IL-TUR contains monolingual (English, Hindi) and multi-lingual (9 Indian languages) domain-specific tasks that address different aspects of the legal system from the point of view of understanding and reasoning over Indian legal documents. We present baseline models (including LLM-based) for each task, outlining the gap between models and the ground truth. To foster further research in the legal domain, we create a leaderboard (available at: https://exploration-lab.github.io/IL-TUR/) where the research community can upload and compare legal text understanding systems.
|
http://arxiv.org/pdf/2407.05399v1
|
[
"Abhinav Joshi",
"Shounak Paul",
"Akshat Sharma",
"Pawan Goyal",
"Saptarshi Ghosh",
"Ashutosh Modi"
] |
2024-07-07T14:55:04Z
|
2024-07-07T14:55:04Z
|
2407.05398
|
A Fair Post-Processing Method based on the MADD Metric for Predictive
Student Models
|
Predictive student models are increasingly used in learning environments. However, due to the rising social impact of their usage, it is now all the more important for these models to be both sufficiently accurate and fair in their predictions. To evaluate algorithmic fairness, a new metric has been developed in education, namely the Model Absolute Density Distance (MADD). This metric enables us to measure how different a predictive model behaves regarding two groups of students, in order to quantify its algorithmic unfairness. In this paper, we thus develop a post-processing method based on this metric, that aims at improving the fairness while preserving the accuracy of relevant predictive models' results. We experiment with our approach on the task of predicting student success in an online course, using both simulated and real-world educational data, and obtain successful results. Our source code and data are in open access at https://github.com/melinaverger/MADD .
|
http://arxiv.org/pdf/2407.05398v1
|
[
"Mélina Verger",
"Chunyang Fan",
"Sébastien Lallé",
"François Bouchet",
"Vanda Luengo"
] |
2024-07-07T14:53:41Z
|
2024-07-07T14:53:41Z
|
2405.12016
|
Strategy-Proof Auctions through Conformal Prediction
|
Auctions are key for maximizing sellers' revenue and ensuring truthful bidding among buyers. Recently, an approach known as differentiable economics based on deep learning shows promise in learning optimal auction mechanisms for multiple items and participants. However, this approach has no guarantee of strategy-proofness at test time. Strategy-proofness is crucial as it ensures that buyers are incentivized to bid their true valuations, leading to optimal and fair auction outcomes without the risk of manipulation. Building on conformal prediction, we introduce a novel approach to achieve strategy-proofness with rigorous statistical guarantees. The key novelties of our method are: (i) the formulation of a regret prediction model, used to quantify at test time violations of strategy-proofness; and (ii) an auction acceptance rule that leverages the predicted regret to ensure that for a new auction, the data-driven mechanism meets the strategy-proofness requirement with high probability (e.g., 99%). Numerical experiments demonstrate the necessity for rigorous guarantees, the validity of our theoretical results, and the applicability of our proposed method.
|
http://arxiv.org/pdf/2405.12016v3
|
[
"Roy Maor Lotan",
"Inbal Talgam-Cohen",
"Yaniv Romano"
] |
2024-07-07T14:48:38Z
|
2024-05-20T13:39:58Z
|
2303.01560
|
Active Learning and Bayesian Optimization: a Unified Perspective to
Learn with a Goal
|
Science and Engineering applications are typically associated with expensive optimization problems to identify optimal design solutions and states of the system of interest. Bayesian optimization and active learning compute surrogate models through efficient adaptive sampling schemes to assist and accelerate this search task toward a given optimization goal. Both those methodologies are driven by specific infill/learning criteria which quantify the utility with respect to the set goal of evaluating the objective function for unknown combinations of optimization variables. While the two fields have seen an exponential growth in popularity in the past decades, their dualism and synergy have received relatively little attention to date. This paper discusses and formalizes the synergy between Bayesian optimization and active learning as symbiotic adaptive sampling methodologies driven by common principles. In particular, we demonstrate this unified perspective through the formalization of the analogy between the Bayesian infill criteria and active learning criteria as driving principles of both the goal-driven procedures. To support our original perspective, we propose a general classification of adaptive sampling techniques to highlight similarities and differences between the vast families of adaptive sampling, active learning, and Bayesian optimization. Accordingly, the synergy is demonstrated mapping the Bayesian infill criteria with the active learning criteria, and is formalized for searches informed by both a single information source and multiple levels of fidelity. In addition, we provide guidelines to apply those learning criteria investigating the performance of different Bayesian schemes for a variety of benchmark problems to highlight benefits and limitations over mathematical properties that characterize real-world applications.
|
http://arxiv.org/abs/2303.01560v4
|
[
"Francesco Di Fiore",
"Michela Nardelli",
"Laura Mainini"
] |
2024-07-07T14:38:37Z
|
2023-03-02T20:22:40Z
|
2306.00061
|
Shadows of quantum machine learning
|
Quantum machine learning is often highlighted as one of the most promising practical applications for which quantum computers could provide a computational advantage. However, a major obstacle to the widespread use of quantum machine learning models in practice is that these models, even once trained, still require access to a quantum computer in order to be evaluated on new data. To solve this issue, we introduce a new class of quantum models where quantum resources are only required during training, while the deployment of the trained model is classical. Specifically, the training phase of our models ends with the generation of a 'shadow model' from which the classical deployment becomes possible. We prove that: i) this class of models is universal for classically-deployed quantum machine learning; ii) it does have restricted learning capacities compared to 'fully quantum' models, but nonetheless iii) it achieves a provable learning advantage over fully classical learners, contingent on widely-believed assumptions in complexity theory. These results provide compelling evidence that quantum machine learning can confer learning advantages across a substantially broader range of scenarios, where quantum computers are exclusively employed during the training phase. By enabling classical deployment, our approach facilitates the implementation of quantum machine learning models in various practical contexts.
|
http://arxiv.org/abs/2306.00061v2
|
[
"Sofiene Jerbi",
"Casper Gyurik",
"Simon C. Marshall",
"Riccardo Molteni",
"Vedran Dunjko"
] |
2024-07-07T14:33:43Z
|
2023-05-31T18:00:02Z
|
2310.15017
|
Mind the Model, Not the Agent: The Primacy Bias in Model-based RL
|
The primacy bias in model-free reinforcement learning (MFRL), which refers to the agent's tendency to overfit early data and lose the ability to learn from new data, can significantly decrease the performance of MFRL algorithms. Previous studies have shown that employing simple techniques, such as resetting the agent's parameters, can substantially alleviate the primacy bias in MFRL. However, the primacy bias in model-based reinforcement learning (MBRL) remains unexplored. In this work, we focus on investigating the primacy bias in MBRL. We begin by observing that resetting the agent's parameters harms its performance in the context of MBRL. We further find that the primacy bias in MBRL is more closely related to the primacy bias of the world model instead of the primacy bias of the agent. Based on this finding, we propose textit{world model resetting}, a simple yet effective technique to alleviate the primacy bias in MBRL. We apply our method to two different MBRL algorithms, MBPO and DreamerV2. We validate the effectiveness of our method on multiple continuous control tasks on MuJoCo and DeepMind Control Suite, as well as discrete control tasks on Atari 100k benchmark. The experimental results show that textit{world model resetting} can significantly alleviate the primacy bias in the model-based setting and improve the algorithm's performance. We also give a guide on how to perform textit{world model resetting} effectively.
|
http://arxiv.org/pdf/2310.15017v2
|
[
"Zhongjian Qiao",
"Jiafei Lyu",
"Xiu Li"
] |
2024-07-07T14:32:02Z
|
2023-10-23T15:12:20Z
|
2407.05385
|
Harmony in Diversity: Merging Neural Networks with Canonical Correlation
Analysis
|
Combining the predictions of multiple trained models through ensembling is generally a good way to improve accuracy by leveraging the different learned features of the models, however it comes with high computational and storage costs. Model fusion, the act of merging multiple models into one by combining their parameters reduces these costs but doesn't work as well in practice. Indeed, neural network loss landscapes are high-dimensional and non-convex and the minima found through learning are typically separated by high loss barriers. Numerous recent works have been focused on finding permutations matching one network features to the features of a second one, lowering the loss barrier on the linear path between them in parameter space. However, permutations are restrictive since they assume a one-to-one mapping between the different models' neurons exists. We propose a new model merging algorithm, CCA Merge, which is based on Canonical Correlation Analysis and aims to maximize the correlations between linear combinations of the model features. We show that our alignment method leads to better performances than past methods when averaging models trained on the same, or differing data splits. We also extend this analysis into the harder setting where more than 2 models are merged, and we find that CCA Merge works significantly better than past methods. Our code is publicly available at https://github.com/shoroi/align-n-merge
|
http://arxiv.org/pdf/2407.05385v1
|
[
"Stefan Horoi",
"Albert Manuel Orozco Camacho",
"Eugene Belilovsky",
"Guy Wolf"
] |
2024-07-07T14:21:04Z
|
2024-07-07T14:21:04Z
|
2406.19136
|
YZS-model: A Predictive Model for Organic Drug Solubility Based on Graph
Convolutional Networks and Transformer-Attention
|
The accurate prediction of drug molecule solubility is essential for determining their therapeutic effectiveness and safety, influencing the drug's ADME processes. Traditional solubility prediction techniques often fail to capture the complex nature of molecular tructures, leading to notable deviations between predictions and actual results. For example, the Discussion on Advanced Drug-Like Compound Structures. Lusci highlighted issues in capturing crucial cyclic structural information in molecules with ring structures. To overcome this issue, our research introduces a novel deep learning framework combining attention-based transformers, Long Short-Term Memory (LSTM) networks, and Graph Convolutional Networks (GCN), aimed at enhancing the precision of solubility predictions. Utilizing a training set of 9,943 compounds and testing on an anticancer compound dataset, our method achieved a correlation coefficient ($R^2$) of 0.59 and a Root Mean Square Error (RMSE) of 0.57, which outperforms the benchmark models' scores of 0.52 ($R^2$) and 0.61 (RMSE). Importantly, in an additional independent test, our model significantly outperformed the baseline with an RMSE of 1.05 compared to 1.28, a relative accuracy improvement of 45.9%. This research not only demonstrates the vast potential of deep learning for improving solubility prediction accuracy but also offers novel insights for drug design and selection in the future. Continued efforts will be directed towards optimizing the model architecture and extending its application to better support the drug development process, underscoring the pivotal role of deep learning in drug discovery.
|
http://arxiv.org/pdf/2406.19136v3
|
[
"Chenxu Wang",
"Haowei Ming",
"Jian He",
"Yao Lu"
] |
2024-07-07T14:10:38Z
|
2024-06-27T12:40:29Z
|
2407.05379
|
AiGAS-dEVL: An Adaptive Incremental Neural Gas Model for Drifting Data
Streams under Extreme Verification Latency
|
The ever-growing speed at which data are generated nowadays, together with the substantial cost of labeling processes cause Machine Learning models to face scenarios in which data are partially labeled. The extreme case where such a supervision is indefinitely unavailable is referred to as extreme verification latency. On the other hand, in streaming setups data flows are affected by exogenous factors that yield non-stationarities in the patterns (concept drift), compelling models learned incrementally from the data streams to adapt their modeled knowledge to the concepts within the stream. In this work we address the casuistry in which these two conditions occur together, by which adaptation mechanisms to accommodate drifts within the stream are challenged by the lack of supervision, requiring further mechanisms to track the evolution of concepts in the absence of verification. To this end we propose a novel approach, AiGAS-dEVL (Adaptive Incremental neural GAS model for drifting Streams under Extreme Verification Latency), which relies on growing neural gas to characterize the distributions of all concepts detected within the stream over time. Our approach exposes that the online analysis of the behavior of these prototypical points over time facilitates the definition of the evolution of concepts in the feature space, the detection of changes in their behavior, and the design of adaptation policies to mitigate the effect of such changes in the model. We assess the performance of AiGAS-dEVL over several synthetic datasets, comparing it to that of state-of-the-art approaches proposed in the recent past to tackle this stream learning setup. Our results reveal that AiGAS-dEVL performs competitively with respect to the rest of baselines, exhibiting a superior adaptability over several datasets in the benchmark while ensuring a simple and interpretable instance-based adaptation strategy.
|
http://arxiv.org/pdf/2407.05379v1
|
[
"Maria Arostegi",
"Miren Nekane Bilbao",
"Jesus L. Lobo",
"Javier Del Ser"
] |
2024-07-07T14:04:57Z
|
2024-07-07T14:04:57Z
|
2402.16918
|
m2mKD: Module-to-Module Knowledge Distillation for Modular Transformers
|
Modular neural architectures are gaining attention for their powerful generalization and efficient adaptation to new domains. However, training these models poses challenges due to optimization difficulties arising from intrinsic sparse connectivity. Leveraging knowledge from monolithic models through techniques like knowledge distillation can facilitate training and enable integration of diverse knowledge. Nevertheless, conventional knowledge distillation approaches are not tailored to modular models and struggle with unique architectures and enormous parameter counts. Motivated by these challenges, we propose module-to-module knowledge distillation (m2mKD) for transferring knowledge between modules. m2mKD combines teacher modules of a pretrained monolithic model and student modules of a modular model with a shared meta model respectively to encourage the student module to mimic the behaviour of the teacher module. We evaluate m2mKD on two modular neural architectures: Neural Attentive Circuits (NACs) and Vision Mixture-of-Experts (V-MoE). Applying m2mKD to NACs yields significant improvements in IID accuracy on Tiny-ImageNet (up to 5.6%) and OOD robustness on Tiny-ImageNet-R (up to 4.2%). Additionally, the V-MoE-Base model trained with m2mKD achieves 3.5% higher accuracy than end-to-end training on ImageNet-1k. Code is available at https://github.com/kamanphoebe/m2mKD.
|
http://arxiv.org/pdf/2402.16918v3
|
[
"Ka Man Lo",
"Yiming Liang",
"Wenyu Du",
"Yuantao Fan",
"Zili Wang",
"Wenhao Huang",
"Lei Ma",
"Jie Fu"
] |
2024-07-07T14:03:04Z
|
2024-02-26T04:47:32Z
|
2407.05375
|
Online Drift Detection with Maximum Concept Discrepancy
|
Continuous learning from an immense volume of data streams becomes exceptionally critical in the internet era. However, data streams often do not conform to the same distribution over time, leading to a phenomenon called concept drift. Since a fixed static model is unreliable for inferring concept-drifted data streams, establishing an adaptive mechanism for detecting concept drift is crucial. Current methods for concept drift detection primarily assume that the labels or error rates of downstream models are given and/or underlying statistical properties exist in data streams. These approaches, however, struggle to address high-dimensional data streams with intricate irregular distribution shifts, which are more prevalent in real-world scenarios. In this paper, we propose MCD-DD, a novel concept drift detection method based on maximum concept discrepancy, inspired by the maximum mean discrepancy. Our method can adaptively identify varying forms of concept drift by contrastive learning of concept embeddings without relying on labels or statistical properties. With thorough experiments under synthetic and real-world scenarios, we demonstrate that the proposed method outperforms existing baselines in identifying concept drifts and enables qualitative analysis with high explainability.
|
http://arxiv.org/pdf/2407.05375v1
|
[
"Ke Wan",
"Yi Liang",
"Susik Yoon"
] |
2024-07-07T13:57:50Z
|
2024-07-07T13:57:50Z
|
2407.05370
|
Learning Label Refinement and Threshold Adjustment for Imbalanced
Semi-Supervised Learning
|
Semi-supervised learning (SSL) algorithms struggle to perform well when exposed to imbalanced training data. In this scenario, the generated pseudo-labels can exhibit a bias towards the majority class, and models that employ these pseudo-labels can further amplify this bias. Here we investigate pseudo-labeling strategies for imbalanced SSL including pseudo-label refinement and threshold adjustment, through the lens of statistical analysis. We find that existing SSL algorithms which generate pseudo-labels using heuristic strategies or uncalibrated model confidence are unreliable when imbalanced class distributions bias pseudo-labels. To address this, we introduce SEmi-supervised learning with pseudo-label optimization based on VALidation data (SEVAL) to enhance the quality of pseudo-labelling for imbalanced SSL. We propose to learn refinement and thresholding parameters from a partition of the training dataset in a class-balanced way. SEVAL adapts to specific tasks with improved pseudo-labels accuracy and ensures pseudo-labels correctness on a per-class basis. Our experiments show that SEVAL surpasses state-of-the-art SSL methods, delivering more accurate and effective pseudo-labels in various imbalanced SSL situations. SEVAL, with its simplicity and flexibility, can enhance various SSL techniques effectively. The code is publicly available~footnote{url{https://github.com/ZerojumpLine/SEVAL}}.
|
http://arxiv.org/pdf/2407.05370v1
|
[
"Zeju Li",
"Ying-Qiu Zheng",
"Chen Chen",
"Saad Jbabdi"
] |
2024-07-07T13:46:22Z
|
2024-07-07T13:46:22Z
|
2306.05014
|
Learning Closed-form Equations for Subgrid-scale Closures from
High-fidelity Data: Promises and Challenges
|
There is growing interest in discovering interpretable, closed-form equations for subgrid-scale (SGS) closures/parameterizations of complex processes in Earth systems. Here, we apply a common equation-discovery technique with expansive libraries to learn closures from filtered direct numerical simulations of 2D turbulence and Rayleigh-B'enard convection (RBC). Across common filters (e.g., Gaussian, box), we robustly discover closures of the same form for momentum and heat fluxes. These closures depend on nonlinear combinations of gradients of filtered variables, with constants that are independent of the fluid/flow properties and only depend on filter type/size. We show that these closures are the nonlinear gradient model (NGM), which is derivable analytically using Taylor-series. Indeed, we suggest that with common (physics-free) equation-discovery algorithms, for many common systems/physics, discovered closures are consistent with the leading term of the Taylor-series (except when cutoff filters are used). Like previous studies, we find that large-eddy simulations with NGM closures are unstable, despite significant similarities between the true and NGM-predicted fluxes (correlations $> 0.95$). We identify two shortcomings as reasons for these instabilities: in 2D, NGM produces zero kinetic energy transfer between resolved and subgrid scales, lacking both diffusion and backscattering. In RBC, potential energy backscattering is poorly predicted. Moreover, we show that SGS fluxes diagnosed from data, presumed the ''truth'' for discovery, depend on filtering procedures and are not unique. Accordingly, to learn accurate, stable closures in future work, we propose several ideas around using physics-informed libraries, loss functions, and metrics. These findings are relevant to closure modeling of any multi-scale system.
|
http://arxiv.org/abs/2306.05014v3
|
[
"Karan Jakhar",
"Yifei Guan",
"Rambod Mojgani",
"Ashesh Chattopadhyay",
"Pedram Hassanzadeh"
] |
2024-07-07T13:40:20Z
|
2023-06-08T08:07:54Z
|
2403.10573
|
Medical Unlearnable Examples: Securing Medical Data from Unauthorized
Training via Sparsity-Aware Local Masking
|
The rapid expansion of AI in healthcare has led to a surge in medical data generation and storage, boosting medical AI development. However, fears of unauthorized use, like training commercial AI models, hinder researchers from sharing their valuable datasets. To encourage data sharing, one promising solution is to introduce imperceptible noise into the data. This method aims to safeguard the data against unauthorized training by inducing degradation in the generalization ability of the trained model. However, they are not effective and efficient when applied to medical data, mainly due to the ignorance of the sparse nature of medical images. To address this problem, we propose the Sparsity-Aware Local Masking (SALM) method, a novel approach that selectively perturbs significant pixel regions rather than the entire image as previously. This simple yet effective approach, by focusing on local areas, significantly narrows down the search space for disturbances and fully leverages the characteristics of sparsity. Our extensive experiments across various datasets and model architectures demonstrate that SALM effectively prevents unauthorized training of different models and outperforms previous SoTA data protection methods.
|
http://arxiv.org/pdf/2403.10573v2
|
[
"Weixiang Sun",
"Yixin Liu",
"Zhiling Yan",
"Kaidi Xu",
"Lichao Sun"
] |
2024-07-07T13:36:22Z
|
2024-03-15T02:35:36Z
|
2402.15259
|
Open Ad Hoc Teamwork with Cooperative Game Theory
|
Ad hoc teamwork poses a challenging problem, requiring the design of an agent to collaborate with teammates without prior coordination or joint training. Open ad hoc teamwork (OAHT) further complicates this challenge by considering environments with a changing number of teammates, referred to as open teams. One promising solution in practice to this problem is leveraging the generalizability of graph neural networks to handle an unrestricted number of agents with various agent-types, named graph-based policy learning (GPL). However, its joint Q-value representation over a coordination graph lacks convincing explanations. In this paper, we establish a new theory to understand the representation of the joint Q-value for OAHT and its learning paradigm, through the lens of cooperative game theory. Building on our theory, we propose a novel algorithm named CIAO, based on GPL's framework, with additional provable implementation tricks that can facilitate learning. The demos of experimental results are available on https://sites.google.com/view/ciao2024, and the code of experiments is published on https://github.com/hsvgbkhgbv/CIAO.
|
http://arxiv.org/pdf/2402.15259v5
|
[
"Jianhong Wang",
"Yang Li",
"Yuan Zhang",
"Wei Pan",
"Samuel Kaski"
] |
2024-07-07T12:43:35Z
|
2024-02-23T11:04:33Z
|
2407.07918
|
Detecting new obfuscated malware variants: A lightweight and
interpretable machine learning approach
|
Machine learning has been successfully applied in developing malware detection systems, with a primary focus on accuracy, and increasing attention to reducing computational overhead and improving model interpretability. However, an important question remains underexplored: How well can machine learning-based models detect entirely new forms of malware not present in the training data? In this study, we present a machine learning-based system for detecting obfuscated malware that is not only highly accurate, lightweight and interpretable, but also capable of successfully adapting to new types of malware attacks. Our system is capable of detecting 15 malware subtypes despite being exclusively trained on one malware subtype, namely the Transponder from the Spyware family. This system was built after training 15 distinct random forest-based models, each on a different malware subtype from the CIC-MalMem-2022 dataset. These models were evaluated against the entire range of malware subtypes, including all unseen malware subtypes. To maintain the system's streamlined nature, training was confined to the top five most important features, which also enhanced interpretability. The Transponder-focused model exhibited high accuracy, exceeding 99.8%, with an average processing speed of 5.7 microseconds per file. We also illustrate how the Shapley additive explanations technique can facilitate the interpretation of the model predictions. Our research contributes to advancing malware detection methodologies, pioneering the feasibility of detecting obfuscated malware by exclusively training a model on a single or a few carefully selected malware subtypes and applying it to detect unseen subtypes.
|
http://arxiv.org/pdf/2407.07918v1
|
[
"Oladipo A. Madamidola",
"Felix Ngobigha",
"Adnane Ez-zizi"
] |
2024-07-07T12:41:40Z
|
2024-07-07T12:41:40Z
|
2407.05340
|
Interpreting the Residual Stream of ResNet18
|
A mechanistic understanding of the computations learned by deep neural networks (DNNs) is far from complete. In the domain of visual object recognition, prior research has illuminated inner workings of InceptionV1, but DNNs with different architectures have remained largely unexplored. This work investigates ResNet18 with a particular focus on its residual stream, an architectural mechanism which InceptionV1 lacks. We observe that for a given block, channel features of the stream are updated along a spectrum: either the input feature skips to the output, the block feature overwrites the output, or the output is some mixture between the input and block features. Furthermore, we show that many residual stream channels compute scale invariant representations through a mixture of the input's smaller-scale feature with the block's larger-scale feature. This not only mounts evidence for the universality of scale equivariance, but also presents how the residual stream further implements scale invariance. Collectively, our results begin an interpretation of the residual stream in visual object recognition, finding it to be a flexible feature manager and a medium to build scale invariant representations.
|
http://arxiv.org/pdf/2407.05340v1
|
[
"André Longon"
] |
2024-07-07T12:13:03Z
|
2024-07-07T12:13:03Z
|
2206.15179
|
D2-LRR: A Dual-Decomposed MDLatLRR Approach for Medical Image Fusion
|
In image fusion tasks, an ideal image decomposition method can bring better performance. MDLatLRR has done a great job in this aspect, but there is still exist some space for improvement. Considering that MDLatLRR focuses solely on the detailed parts (salient features) extracted from input images via latent low-rank representation (LatLRR), the basic parts (principal features) extracted by LatLRR are not fully utilized. Therefore, we introduced an enhanced multi-level decomposition method named dual-decomposed MDLatLRR (D2-LRR) which effectively analyzes and utilizes all image features extracted through LatLRR. Specifically, color images are converted into YUV color space and grayscale images, and the Y-channel and grayscale images are input into the trained parameters of LatLRR to obtain the detailed parts containing four rounds of decomposition and the basic parts. Subsequently, the basic parts are fused using an average strategy, while the detail part is fused using kernel norm operation. The fused image is ultimately transformed back into an RGB image, resulting in the final fusion output. We apply D2-LRR to medical image fusion tasks. The detailed parts are fused employing a nuclear-norm operation, while the basic parts are fused using an average strategy. Comparative analyses among existing methods showcase that our proposed approach attains cutting-edge fusion performance in both objective and subjective assessments.
|
http://arxiv.org/pdf/2206.15179v4
|
[
"Xu Song",
"Tianyu Shen",
"Hui Li",
"Xiao-Jun Wu"
] |
2024-07-07T11:39:46Z
|
2022-06-30T10:31:30Z
|
2406.01528
|
Physics-Informed Neural Networks for Dynamic Process Operations with
Limited Physical Knowledge and Data
|
In chemical engineering, process data are expensive to acquire, and complex phenomena are difficult to fully model. We explore the use of physics-informed neural networks (PINNs) for dynamic processes with incomplete mechanistic semi-explicit differential-algebraic equation systems and scarce process data. In particular, we focus on estimating states for which neither direct observational data nor constitutive equations are available. We propose an easy-to-apply heuristic to assess whether estimation of such states may be possible. As numerical examples, we consider a continuously stirred tank reactor and a liquid-liquid separator. We find that PINNs can infer unmeasured states with reasonable accuracy, and they generalize better in low-data scenarios than purely data-driven models. We thus show that PINNs are capable of modeling processes when relatively few experimental data and only partially known mechanistic descriptions are available, and conclude that they constitute a promising avenue that warrants further investigation.
|
http://arxiv.org/pdf/2406.01528v2
|
[
"Mehmet Velioglu",
"Song Zhai",
"Sophia Rupprecht",
"Alexander Mitsos",
"Andreas Jupke",
"Manuel Dahmen"
] |
2024-07-07T11:30:50Z
|
2024-06-03T16:58:17Z
|
2407.05330
|
Fast Proxy Experiment Design for Causal Effect Identification
|
Identifying causal effects is a key problem of interest across many disciplines. The two long-standing approaches to estimate causal effects are observational and experimental (randomized) studies. Observational studies can suffer from unmeasured confounding, which may render the causal effects unidentifiable. On the other hand, direct experiments on the target variable may be too costly or even infeasible to conduct. A middle ground between these two approaches is to estimate the causal effect of interest through proxy experiments, which are conducted on variables with a lower cost to intervene on compared to the main target. Akbari et al. [2022] studied this setting and demonstrated that the problem of designing the optimal (minimum-cost) experiment for causal effect identification is NP-complete and provided a naive algorithm that may require solving exponentially many NP-hard problems as a sub-routine in the worst case. In this work, we provide a few reformulations of the problem that allow for designing significantly more efficient algorithms to solve it as witnessed by our extensive simulations. Additionally, we study the closely-related problem of designing experiments that enable us to identify a given effect through valid adjustments sets.
|
http://arxiv.org/pdf/2407.05330v1
|
[
"Sepehr Elahi",
"Sina Akbari",
"Jalal Etesami",
"Negar Kiyavash",
"Patrick Thiran"
] |
2024-07-07T11:09:38Z
|
2024-07-07T11:09:38Z
|
2407.05315
|
Topological Persistence Guided Knowledge Distillation for Wearable
Sensor Data
|
Deep learning methods have achieved a lot of success in various applications involving converting wearable sensor data to actionable health insights. A common application areas is activity recognition, where deep-learning methods still suffer from limitations such as sensitivity to signal quality, sensor characteristic variations, and variability between subjects. To mitigate these issues, robust features obtained by topological data analysis (TDA) have been suggested as a potential solution. However, there are two significant obstacles to using topological features in deep learning: (1) large computational load to extract topological features using TDA, and (2) different signal representations obtained from deep learning and TDA which makes fusion difficult. In this paper, to enable integration of the strengths of topological methods in deep-learning for time-series data, we propose to use two teacher networks, one trained on the raw time-series data, and another trained on persistence images generated by TDA methods. The distilled student model utilizes only the raw time-series data at test-time. This approach addresses both issues. The use of KD with multiple teachers utilizes complementary information, and results in a compact model with strong supervisory features and an integrated richer representation. To assimilate desirable information from different modalities, we design new constraints, including orthogonality imposed on feature correlation maps for improving feature expressiveness and allowing the student to easily learn from the teacher. Also, we apply an annealing strategy in KD for fast saturation and better accommodation from different features, while the knowledge gap between the teachers and student is reduced. Finally, a robust student model is distilled, which uses only the time-series data as an input, while implicitly preserving topological features.
|
http://arxiv.org/abs/2407.05315v1
|
[
"Eun Som Jeon",
"Hongjun Choi",
"Ankita Shukla",
"Yuan Wang",
"Hyunglae Lee",
"Matthew P. Buman",
"Pavan Turaga"
] |
2024-07-07T10:08:34Z
|
2024-07-07T10:08:34Z
|
2407.00617
|
Iterative Nash Policy Optimization: Aligning LLMs with General
Preferences via No-Regret Learning
|
Reinforcement Learning with Human Feedback (RLHF) has achieved great success in aligning large language models (LLMs) with human preferences. Prevalent RLHF approaches are reward-based, following the Bradley-Terry (BT) model assumption, which may not fully capture the complexity of human preferences. In this paper, we explore RLHF under a general preference framework and approach it from a game-theoretic perspective. Specifically, we formulate the problem as a two-player game and propose a novel algorithm, iterative Nash policy optimization (INPO). The key idea is to let the policy play against itself via no-regret learning, thereby approximating the Nash policy. Unlike previous methods, INPO bypasses the need for estimating the expected win rate for individual responses, which typically incurs high computational or annotation costs. Instead, we introduce a new loss objective that is directly minimized over a preference dataset. We provide theoretical analysis for our approach and demonstrate its effectiveness through experiments on various representative benchmarks. With an LLaMA-3-8B-based SFT model, INPO achieves a 41.5% length-controlled win rate on AlpacaEval 2.0 and a 38.3% win rate on Arena-Hard, showing substantial improvement over the state-of-the-art iterative algorithm [Dong et al., 2024] under the BT model assumption. Additionally, our ablation study highlights the benefits of incorporating KL regularization for response length control.
|
http://arxiv.org/pdf/2407.00617v2
|
[
"Yuheng Zhang",
"Dian Yu",
"Baolin Peng",
"Linfeng Song",
"Ye Tian",
"Mingyue Huo",
"Nan Jiang",
"Haitao Mi",
"Dong Yu"
] |
2024-07-07T09:51:26Z
|
2024-06-30T08:00:34Z
|
2402.07963
|
SPO: Sequential Monte Carlo Policy Optimisation
|
Leveraging planning during learning and decision-making is central to the long-term development of intelligent agents. Recent works have successfully combined tree-based search methods and self-play learning mechanisms to this end. However, these methods typically face scaling challenges due to the sequential nature of their search. While practical engineering solutions can partly overcome this, they often result in a negative impact on performance. In this paper, we introduce SPO: Sequential Monte Carlo Policy Optimisation, a model-based reinforcement learning algorithm grounded within the Expectation Maximisation (EM) framework. We show that SPO provides robust policy improvement and efficient scaling properties. The sample-based search makes it directly applicable to both discrete and continuous action spaces without modifications. We demonstrate statistically significant improvements in performance relative to model-free and model-based baselines across both continuous and discrete environments. Furthermore, the parallel nature of SPO's search enables effective utilisation of hardware accelerators, yielding favourable scaling laws.
|
http://arxiv.org/pdf/2402.07963v2
|
[
"Matthew V Macfarlane",
"Edan Toledo",
"Donal Byrne",
"Paul Duckworth",
"Alexandre Laterre"
] |
2024-07-07T09:48:13Z
|
2024-02-12T10:32:47Z
|
2406.09291
|
A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products
and Graph Coarsening
|
Subgraph Graph Neural Networks (Subgraph GNNs) enhance the expressivity of message-passing GNNs by representing graphs as sets of subgraphs. They have shown impressive performance on several tasks, but their complexity limits applications to larger graphs. Previous approaches suggested processing only subsets of subgraphs, selected either randomly or via learnable sampling. However, they make suboptimal subgraph selections or can only cope with very small subset sizes, inevitably incurring performance degradation. This paper introduces a new Subgraph GNNs framework to address these issues. We employ a graph coarsening function to cluster nodes into super-nodes with induced connectivity. The product between the coarsened and the original graph reveals an implicit structure whereby subgraphs are associated with specific sets of nodes. By running generalized message-passing on such graph product, our method effectively implements an efficient, yet powerful Subgraph GNN. Controlling the coarsening function enables meaningful selection of any number of subgraphs while, contrary to previous methods, being fully compatible with standard training techniques. Notably, we discover that the resulting node feature tensor exhibits new, unexplored permutation symmetries. We leverage this structure, characterize the associated linear equivariant layers and incorporate them into the layers of our Subgraph GNN architecture. Extensive experiments on multiple graph learning benchmarks demonstrate that our method is significantly more flexible than previous approaches, as it can seamlessly handle any number of subgraphs, while consistently outperforming baseline approaches.
|
http://arxiv.org/pdf/2406.09291v2
|
[
"Guy Bar-Shalom",
"Yam Eitan",
"Fabrizio Frasca",
"Haggai Maron"
] |
2024-07-07T09:32:11Z
|
2024-06-13T16:29:06Z
|
2407.05302
|
Mamba Hawkes Process
|
Irregular and asynchronous event sequences are prevalent in many domains, such as social media, finance, and healthcare. Traditional temporal point processes (TPPs), like Hawkes processes, often struggle to model mutual inhibition and nonlinearity effectively. While recent neural network models, including RNNs and Transformers, address some of these issues, they still face challenges with long-term dependencies and computational efficiency. In this paper, we introduce the Mamba Hawkes Process (MHP), which leverages the Mamba state space architecture to capture long-range dependencies and dynamic event interactions. Our results show that MHP outperforms existing models across various datasets. Additionally, we propose the Mamba Hawkes Process Extension (MHP-E), which combines Mamba and Transformer models to enhance predictive capabilities. We present the novel application of the Mamba architecture to Hawkes processes, a flexible and extensible model structure, and a theoretical analysis of the synergy between state space models and Hawkes processes. Experimental results demonstrate the superior performance of both MHP and MHP-E, advancing the field of temporal point process modeling.
|
http://arxiv.org/pdf/2407.05302v1
|
[
"Anningzhe Gao",
"Shan Dai",
"Yan Hu"
] |
2024-07-07T08:37:43Z
|
2024-07-07T08:37:43Z
|
2407.05287
|
Model-agnostic meta-learners for estimating heterogeneous treatment
effects over time
|
Estimating heterogeneous treatment effects (HTEs) over time is crucial in many disciplines such as personalized medicine. For example, electronic health records are commonly collected over several time periods and then used to personalize treatment decisions. Existing works for this task have mostly focused on model-based learners (i.e., learners that adapt specific machine-learning models). In contrast, model-agnostic learners -- so-called meta-learners -- are largely unexplored. In our paper, we propose several meta-learners that are model-agnostic and thus can be used in combination with arbitrary machine learning models (e.g., transformers) to estimate HTEs over time. Here, our focus is on learners that can be obtained via weighted pseudo-outcome regressions, which allows for efficient estimation by targeting the treatment effect directly. We then provide a comprehensive theoretical analysis that characterizes the different learners and that allows us to offer insights into when specific learners are preferable. Finally, we confirm our theoretical insights through numerical experiments. In sum, while meta-learners are already state-of-the-art for the static setting, we are the first to propose a comprehensive set of meta-learners for estimating HTEs in the time-varying setting.
|
http://arxiv.org/pdf/2407.05287v1
|
[
"Dennis Frauen",
"Konstantin Hess",
"Stefan Feuerriegel"
] |
2024-07-07T07:07:48Z
|
2024-07-07T07:07:48Z
|
2407.05286
|
Stability and Generalization for Stochastic Recursive Momentum-based
Algorithms for (Strongly-)Convex One to $K$-Level Stochastic Optimizations
|
STOchastic Recursive Momentum (STORM)-based algorithms have been widely developed to solve one to $K$-level ($K geq 3$) stochastic optimization problems. Specifically, they use estimators to mitigate the biased gradient issue and achieve near-optimal convergence results. However, there is relatively little work on understanding their generalization performance, particularly evident during the transition from one to $K$-level optimization contexts. This paper provides a comprehensive generalization analysis of three representative STORM-based algorithms: STORM, COVER, and SVMR, for one, two, and $K$-level stochastic optimizations under both convex and strongly convex settings based on algorithmic stability. Firstly, we define stability for $K$-level optimizations and link it to generalization. Then, we detail the stability results for three prominent STORM-based algorithms. Finally, we derive their excess risk bounds by balancing stability results with optimization errors. Our theoretical results provide strong evidence to complete STORM-based algorithms: (1) Each estimator may decrease their stability due to variance with its estimation target. (2) Every additional level might escalate the generalization error, influenced by the stability and the variance between its cumulative stochastic gradient and the true gradient. (3) Increasing the batch size for the initial computation of estimators presents a favorable trade-off, enhancing the generalization performance.
|
http://arxiv.org/pdf/2407.05286v1
|
[
"Xiaokang Pan",
"Xingyu Li",
"Jin Liu",
"Tao Sun",
"Kai Sun",
"Lixing Chen",
"Zhe Qu"
] |
2024-07-07T07:07:04Z
|
2024-07-07T07:07:04Z
|
2407.05285
|
Gradient Diffusion: A Perturbation-Resilient Gradient Leakage Attack
|
Recent years have witnessed the vulnerability of Federated Learning (FL) against gradient leakage attacks, where the private training data can be recovered from the exchanged gradients, making gradient protection a critical issue for the FL training process. Existing solutions often resort to perturbation-based mechanisms, such as differential privacy, where each participating client injects a specific amount of noise into local gradients before aggregating to the server, and the global distribution variation finally conceals the gradient privacy. However, perturbation is not always the panacea for gradient protection since the robustness heavily relies on the injected noise. This intuition raises an interesting question: textit{is it possible to deactivate existing protection mechanisms by removing the perturbation inside the gradients?} In this paper, we present the answer: textit{yes} and propose the Perturbation-resilient Gradient Leakage Attack (PGLA), the first attempt to recover the perturbed gradients, without additional access to the original model structure or third-party data. Specifically, we leverage the inherent diffusion property of gradient perturbation protection and construct a novel diffusion-based denoising model to implement PGLA. Our insight is that capturing the disturbance level of perturbation during the diffusion reverse process can release the gradient denoising capability, which promotes the diffusion model to generate approximate gradients as the original clean version through adaptive sampling steps. Extensive experiments demonstrate that PGLA effectively recovers the protected gradients and exposes the FL training process to the threat of gradient leakage, achieving the best quality in gradient denoising and data recovery compared to existing models. We hope to arouse public attention on PGLA and its defense.
|
http://arxiv.org/pdf/2407.05285v1
|
[
"Xuan Liu",
"Siqi Cai",
"Qihua Zhou",
"Song Guo",
"Ruibin Li",
"Kaiwei Lin"
] |
2024-07-07T07:06:49Z
|
2024-07-07T07:06:49Z
|
2402.01057
|
Expert Proximity as Surrogate Rewards for Single Demonstration Imitation
Learning
|
In this paper, we focus on single-demonstration imitation learning (IL), a practical approach for real-world applications where acquiring multiple expert demonstrations is costly or infeasible and the ground truth reward function is not available. In contrast to typical IL settings with multiple demonstrations, single-demonstration IL involves an agent having access to only one expert trajectory. We highlight the issue of sparse reward signals in this setting and propose to mitigate this issue through our proposed Transition Discriminator-based IL (TDIL) method. TDIL is an IRL method designed to address reward sparsity by introducing a denser surrogate reward function that considers environmental dynamics. This surrogate reward function encourages the agent to navigate towards states that are proximal to expert states. In practice, TDIL trains a transition discriminator to differentiate between valid and non-valid transitions in a given environment to compute the surrogate rewards. The experiments demonstrate that TDIL outperforms existing IL approaches and achieves expert-level performance in the single-demonstration IL setting across five widely adopted MuJoCo benchmarks as well as the "Adroit Door" robotic environment.
|
http://arxiv.org/pdf/2402.01057v3
|
[
"Chia-Cheng Chiang",
"Li-Cheng Lan",
"Wei-Fang Sun",
"Chien Feng",
"Cho-Jui Hsieh",
"Chun-Yi Lee"
] |
2024-07-07T06:51:03Z
|
2024-02-01T23:06:19Z
|
2307.08189
|
Mini-Giants: "Small" Language Models and Open Source Win-Win
|
ChatGPT is phenomenal. However, it is prohibitively expensive to train and refine such giant models. Fortunately, small language models are flourishing and becoming more and more competent. We call them "mini-giants". We argue that open source community like Kaggle and mini-giants will win-win in many ways, technically, ethically and socially. In this article, we present a brief yet rich background, discuss how to attain small language models, present a comparative study of small language models and a brief discussion of evaluation methods, discuss the application scenarios where small language models are most needed in the real world, and conclude with discussion and outlook.
|
http://arxiv.org/pdf/2307.08189v2
|
[
"Zhengping Zhou",
"Lezhi Li",
"Xinxi Chen",
"Andy Li"
] |
2024-07-07T06:42:31Z
|
2023-07-17T01:35:56Z
|
2401.14591
|
Ricci flow-guided autoencoders in learning time-dependent dynamics
|
We present a manifold-based autoencoder method for learning dynamics in time, notably partial differential equations (PDEs), in which the manifold latent space evolves according to Ricci flow. This can be accomplished by simulating Ricci flow in a physics-informed setting, and manifold quantities can be matched so that Ricci flow is empirically achieved. With our method, the manifold is discerned through the training procedure, while the latent evolution due to Ricci flow induces a more accommodating representation over static methods. We present our method on a range of experiments consisting of PDE data that encompasses desirable characteristics such as periodicity and randomness. By incorporating latent dynamics, we sustain a manifold latent representation for all values in the ambient PDE time interval. Furthermore, the dynamical manifold latent space facilitates qualities such as learning for out-of-distribution data, and robustness. We showcase our method by demonstrating these features.
|
http://arxiv.org/pdf/2401.14591v8
|
[
"Andrew Gracyk"
] |
2024-07-07T06:10:41Z
|
2024-01-26T01:36:48Z
|
2402.07363
|
Strategically-Robust Learning Algorithms for Bidding in First-Price
Auctions
|
Learning to bid in repeated first-price auctions is a fundamental problem at the interface of game theory and machine learning, which has seen a recent surge in interest due to the transition of display advertising to first-price auctions. In this work, we propose a novel concave formulation for pure-strategy bidding in first-price auctions, and use it to analyze natural Gradient-Ascent-based algorithms for this problem. Importantly, our analysis goes beyond regret, which was the typical focus of past work, and also accounts for the strategic backdrop of online-advertising markets where bidding algorithms are deployed -- we provide the first guarantees of strategic-robustness and incentive-compatibility for Gradient Ascent. Concretely, we show that our algorithms achieve $O(sqrt{T})$ regret when the highest competing bids are generated adversarially, and show that no online algorithm can do better. We further prove that the regret reduces to $O(log T)$ when the competition is stationary and stochastic, which drastically improves upon the previous best of $O(sqrt{T})$. Moving beyond regret, we show that a strategic seller cannot exploit our algorithms to extract more revenue on average than is possible under the optimal mechanism. Finally, we prove that our algorithm is also incentive compatible -- it is a (nearly) dominant strategy for the buyer to report her values truthfully to the algorithm as a whole. Altogether, these guarantees make our algorithms the first to simultaneously achieve both optimal regret and strategic-robustness.
|
http://arxiv.org/pdf/2402.07363v2
|
[
"Rachitesh Kumar",
"Jon Schneider",
"Balasubramanian Sivan"
] |
2024-07-07T06:07:00Z
|
2024-02-12T01:33:33Z
|
2405.08233
|
A Deep Dive into the Factors Influencing Financial Success: A Machine
Learning Approach
|
This paper explores various socioeconomic factors that contribute to individual financial success using machine learning algorithms and approaches. Financial success, a critical aspect of all individual's well-being, is a complex concept influenced by various factors. This study aims to understand the determinants of financial success. It examines the survey data from the National Longitudinal Survey of Youth 1997 by the Bureau of Labor Statistics (1), consisting of a sample of 8,984 individuals's longitudinal data over years. The dataset comprises income variables and a large set of socioeconomic variables of individuals. An in-depth analysis shows the effectiveness of machine learning algorithms in financial success research, highlights the potential of leveraging longitudinal data to enhance prediction accuracy, and provides valuable insights into how various socioeconomic factors influence financial success. The findings highlight the significant influence of highest education degree, occupation and gender as the top three determinants of individual income among socioeconomic factors examined. Yearly working hours, age and work tenure follow as three secondary influencing factors, and all other factors including parental household income, industry, parents' highest grade and others are identified as tertiary factors. These insights allow researchers to better understand the complex nature of financial success, and are also crucial for fostering financial success among individuals and advancing broader societal well-being by providing insights for policymakers during decision-making process.
|
http://arxiv.org/pdf/2405.08233v3
|
[
"Michael Zhou",
"Ramin Ramezani"
] |
2024-07-07T06:01:45Z
|
2024-05-13T23:19:02Z
|
2407.05268
|
Federated Knowledge Transfer Fine-tuning Large Server Model with
Resource-Constrained IoT Clients
|
The training of large models, involving fine-tuning, faces the scarcity of high-quality data. Compared to the solutions based on centralized data centers, updating large models in the Internet of Things (IoT) faces challenges in coordinating knowledge from distributed clients by using their private and heterogeneous data. To tackle such a challenge, we propose KOALA (Federated Knowledge Transfer Fine-tuning Large Server Model with Resource-Constrained IoT Clients) to impel the training of large models in IoT. Since the resources obtained by IoT clients are limited and restricted, it is infeasible to locally execute large models and also update them in a privacy-preserving manner. Therefore, we leverage federated learning and knowledge distillation to update large models through collaboration with their small models, which can run locally at IoT clients to process their private data separately and enable large-small model knowledge transfer through iterative learning between the server and clients. Moreover, to support clients with similar or different computing capacities, KOALA is designed with two kinds of large-small model joint learning modes, namely to be homogeneous or heterogeneous. Experimental results demonstrate that compared to the conventional approach, our method can not only achieve similar training performance but also significantly reduce the need for local storage and computing power resources.
|
http://arxiv.org/pdf/2407.05268v1
|
[
"Shaoyuan Chen",
"Linlin You",
"Rui Liu",
"Shuo Yu",
"Ahmed M. Abdelmoniem"
] |
2024-07-07T05:46:01Z
|
2024-07-07T05:46:01Z
|
2407.05262
|
FastSpiker: Enabling Fast Training for Spiking Neural Networks on
Event-based Data through Learning Rate Enhancements for Autonomous Embedded
Systems
|
Autonomous embedded systems (e.g., robots) typically necessitate intelligent computation with low power/energy processing for completing their tasks. Such requirements can be fulfilled by embodied neuromorphic intelligence with spiking neural networks (SNNs) because of their high learning quality (e.g., accuracy) and sparse computation. Here, the employment of event-based data is preferred to ensure seamless connectivity between input and processing parts. However, state-of-the-art SNNs still face a long training time to achieve high accuracy, thereby incurring high energy consumption and producing a high rate of carbon emission. Toward this, we propose FastSpiker, a novel methodology that enables fast SNN training on event-based data through learning rate enhancements targeting autonomous embedded systems. In FastSpiker, we first investigate the impact of different learning rate policies and their values, then select the ones that quickly offer high accuracy. Afterward, we explore different settings for the selected learning rate policies to find the appropriate policies through a statistical-based decision. Experimental results show that our FastSpiker offers up to 10.5x faster training time and up to 88.39% lower carbon emission to achieve higher or comparable accuracy to the state-of-the-art on the event-based automotive dataset (i.e., NCARS). In this manner, our FastSpiker methodology paves the way for green and sustainable computing in realizing embodied neuromorphic intelligence for autonomous embedded systems.
|
http://arxiv.org/pdf/2407.05262v1
|
[
"Iqra Bano",
"Rachmad Vidya Wicaksana Putra",
"Alberto Marchisio",
"Muhammad Shafique"
] |
2024-07-07T05:17:17Z
|
2024-07-07T05:17:17Z
|
2407.05261
|
Disciplined Geodesically Convex Programming
|
Convex programming plays a fundamental role in machine learning, data science, and engineering. Testing convexity structure in nonlinear programs relies on verifying the convexity of objectives and constraints. citet{grant2006disciplined} introduced a framework, Disciplined Convex Programming (DCP), for automating this verification task for a wide range of convex functions that can be decomposed into basic convex functions (atoms) using convexity-preserving compositions and transformations (rules). However, the restriction to Euclidean convexity concepts can limit the applicability of the framework. For instance, many notable instances of statistical estimators and matrix-valued (sub)routines in machine learning applications are Euclidean non-convex, but exhibit geodesic convexity through a more general Riemannian lens. In this work, we extend disciplined programming to this setting by introducing Disciplined Geodesically Convex Programming (DGCP). We determine convexity-preserving compositions and transformations for geodesically convex functions on general Cartan-Hadamard manifolds, as well as for the special case of symmetric positive definite matrices, a common setting in matrix-valued optimization. For the latter, we also define a basic set of atoms. Our paper is accompanied by a Julia package SymbolicAnalysis.jl, which provides functionality for testing and certifying DGCP-compliant expressions. Our library interfaces with manifold optimization software, which allows for directly solving verified geodesically convex programs.
|
http://arxiv.org/pdf/2407.05261v1
|
[
"Andrew Cheng",
"Vaibhav Dixit",
"Melanie Weber"
] |
2024-07-07T05:13:51Z
|
2024-07-07T05:13:51Z
|
2407.05259
|
Multi-scale Conditional Generative Modeling for Microscopic Image
Restoration
|
The advance of diffusion-based generative models in recent years has revolutionized state-of-the-art (SOTA) techniques in a wide variety of image analysis and synthesis tasks, whereas their adaptation on image restoration, particularly within computational microscopy remains theoretically and empirically underexplored. In this research, we introduce a multi-scale generative model that enhances conditional image restoration through a novel exploitation of the Brownian Bridge process within wavelet domain. By initiating the Brownian Bridge diffusion process specifically at the lowest-frequency subband and applying generative adversarial networks at subsequent multi-scale high-frequency subbands in the wavelet domain, our method provides significant acceleration during training and sampling while sustaining a high image generation quality and diversity on par with SOTA diffusion models. Experimental results on various computational microscopy and imaging tasks confirm our method's robust performance and its considerable reduction in its sampling steps and time. This pioneering technique offers an efficient image restoration framework that harmonizes efficiency with quality, signifying a major stride in incorporating cutting-edge generative models into computational microscopy workflows.
|
http://arxiv.org/pdf/2407.05259v1
|
[
"Luzhe Huang",
"Xiongye Xiao",
"Shixuan Li",
"Jiawen Sun",
"Yi Huang",
"Aydogan Ozcan",
"Paul Bogdan"
] |
2024-07-07T05:11:00Z
|
2024-07-07T05:11:00Z
|
2404.04970
|
How to characterize imprecision in multi-view clustering?
|
It is still challenging to cluster multi-view data since existing methods can only assign an object to a specific (singleton) cluster when combining different view information. As a result, it fails to characterize imprecision of objects in overlapping regions of different clusters, thus leading to a high risk of errors. In this paper, we thereby want to answer the question: how to characterize imprecision in multi-view clustering? Correspondingly, we propose a multi-view low-rank evidential c-means based on entropy constraint (MvLRECM). The proposed MvLRECM can be considered as a multi-view version of evidential c-means based on the theory of belief functions. In MvLRECM, each object is allowed to belong to different clusters with various degrees of support (masses of belief) to characterize uncertainty when decision-making. Moreover, if an object is in the overlapping region of several singleton clusters, it can be assigned to a meta-cluster, defined as the union of these singleton clusters, to characterize the local imprecision in the result. In addition, entropy-weighting and low-rank constraints are employed to reduce imprecision and improve accuracy. Compared to state-of-the-art methods, the effectiveness of MvLRECM is demonstrated based on several toy and UCI real datasets.
|
http://arxiv.org/pdf/2404.04970v2
|
[
"Jinyi Xu",
"Zuowei Zhang",
"Ze Lin",
"Yixiang Chen",
"Zhe Liu",
"Weiping Ding"
] |
2024-07-07T04:47:49Z
|
2024-04-07T14:20:51Z
|
2111.08211
|
FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining
Competitive Performance in Federated Learning
|
Federated learning (FL) aims to protect data privacy by enabling clients to build machine learning models collaboratively without sharing their private data. Recent works demonstrate that information exchanged during FL is subject to gradient-based privacy attacks, and consequently, a variety of privacy-preserving methods have been adopted to thwart such attacks. However, these defensive methods either introduce orders of magnitude more computational and communication overheads (e.g., with homomorphic encryption) or incur substantial model performance losses in terms of prediction accuracy (e.g., with differential privacy). In this work, we propose $textsc{FedCG}$, a novel federated learning method that leverages conditional generative adversarial networks to achieve high-level privacy protection while still maintaining competitive model performance. $textsc{FedCG}$ decomposes each client's local network into a private extractor and a public classifier and keeps the extractor local to protect privacy. Instead of exposing extractors, $textsc{FedCG}$ shares clients' generators with the server for aggregating clients' shared knowledge, aiming to enhance the performance of each client's local networks. Extensive experiments demonstrate that $textsc{FedCG}$ can achieve competitive model performance compared with FL baselines, and privacy analysis shows that $textsc{FedCG}$ has a high-level privacy-preserving capability. Code is available at https://github.com/yankang18/FedCG
|
http://arxiv.org/abs/2111.08211v3
|
[
"Yuezhou Wu",
"Yan Kang",
"Jiahuan Luo",
"Yuanqin He",
"Qiang Yang"
] |
2024-07-07T03:57:12Z
|
2021-11-16T03:20:37Z
|
2402.15198
|
Bidirectional Uncertainty-Based Active Learning for Open Set Annotation
|
Active learning (AL) in open set scenarios presents a novel challenge of identifying the most valuable examples in an unlabeled data pool that comprises data from both known and unknown classes. Traditional methods prioritize selecting informative examples with low confidence, with the risk of mistakenly selecting unknown-class examples with similarly low confidence. Recent methods favor the most probable known-class examples, with the risk of picking simple already mastered examples. In this paper, we attempt to query examples that are both likely from known classes and highly informative, and propose a Bidirectional Uncertainty-based Active Learning (BUAL) framework. Specifically, we achieve this by first pushing the unknown class examples toward regions with high-confidence predictions, i.e., the proposed Random Label Negative Learning method. Then, we propose a Bidirectional Uncertainty sampling strategy by jointly estimating uncertainty posed by both positive and negative learning to perform consistent and stable sampling. BUAL successfully extends existing uncertainty-based AL methods to complex open-set scenarios. Extensive experiments on multiple datasets with varying openness demonstrate that BUAL achieves state-of-the-art performance. The code is available at https://github.com/chenchenzong/BUAL.
|
http://arxiv.org/pdf/2402.15198v2
|
[
"Chen-Chen Zong",
"Ye-Wen Wang",
"Kun-Peng Ning",
"Hai-Bo Ye",
"Sheng-Jun Huang"
] |
2024-07-07T03:48:33Z
|
2024-02-23T08:59:04Z
|
2406.16087
|
Imperative Learning: A Self-supervised Neural-Symbolic Learning
Framework for Robot Autonomy
|
Data-driven methods such as reinforcement and imitation learning have achieved remarkable success in robot autonomy. However, their data-centric nature still hinders them from generalizing well to ever-changing environments. Moreover, collecting large datasets for robotic tasks is often impractical and expensive. To overcome these challenges, we introduce a new self-supervised neural-symbolic (NeSy) computational framework, imperative learning (IL), for robot autonomy, leveraging the generalization abilities of symbolic reasoning. The framework of IL consists of three primary components: a neural module, a reasoning engine, and a memory system. We formulate IL as a special bilevel optimization (BLO), which enables reciprocal learning over the three modules. This overcomes the label-intensive obstacles associated with data-driven approaches and takes advantage of symbolic reasoning concerning logical reasoning, physical principles, geometric analysis, etc. We discuss several optimization techniques for IL and verify their effectiveness in five distinct robot autonomy tasks including path planning, rule induction, optimal control, visual odometry, and multi-robot routing. Through various experiments, we show that IL can significantly enhance robot autonomy capabilities and we anticipate that it will catalyze further research across diverse domains.
|
http://arxiv.org/pdf/2406.16087v2
|
[
"Chen Wang",
"Kaiyi Ji",
"Junyi Geng",
"Zhongqiang Ren",
"Taimeng Fu",
"Fan Yang",
"Yifan Guo",
"Haonan He",
"Xiangyu Chen",
"Zitong Zhan",
"Qiwei Du",
"Shaoshu Su",
"Bowen Li",
"Yuheng Qiu",
"Yi Du",
"Qihang Li",
"Yifan Yang",
"Xiao Lin",
"Zhipeng Zhao"
] |
2024-07-07T03:20:26Z
|
2024-06-23T12:02:17Z
|
2407.05237
|
Privacy of the last iterate in cyclically-sampled DP-SGD on nonconvex
composite losses
|
Differentially private stochastic gradient descent (DP-SGD) refers to a family of optimization algorithms that provide a guaranteed level of differential privacy (DP) through DP accounting techniques. However, current accounting techniques make assumptions that diverge significantly from practical DP-SGD implementations. For example, they may assume the loss function is Lipschitz continuous and convex, sample the batches randomly with replacement, or omit the gradient clipping step. In this work, we analyze the most commonly used variant of DP-SGD, in which we sample batches cyclically with replacement, perform gradient clipping, and only release the last DP-SGD iterate. More specifically - without assuming convexity, smoothness, or Lipschitz continuity of the loss function - we establish new R'enyi differential privacy (RDP) bounds for the last DP-SGD iterate under the mild assumption that (i) the DP-SGD stepsize is small relative to the topological constants in the loss function, and (ii) the loss function is weakly-convex. Moreover, we show that our bounds converge to previously established convex bounds when the weak-convexity parameter of the objective function approaches zero. In the case of non-Lipschitz smooth loss functions, we provide a weaker bound that scales well in terms of the number of DP-SGD iterations.
|
http://arxiv.org/pdf/2407.05237v1
|
[
"Weiwei Kong",
"Mónica Ribero"
] |
2024-07-07T02:35:55Z
|
2024-07-07T02:35:55Z
|
2407.05232
|
PAPM: A Physics-aware Proxy Model for Process Systems
|
In the context of proxy modeling for process systems, traditional data-driven deep learning approaches frequently encounter significant challenges, such as substantial training costs induced by large amounts of data, and limited generalization capabilities. As a promising alternative, physics-aware models incorporate partial physics knowledge to ameliorate these challenges. Although demonstrating efficacy, they fall short in terms of exploration depth and universality. To address these shortcomings, we introduce a physics-aware proxy model (PAPM) that fully incorporates partial prior physics of process systems, which includes multiple input conditions and the general form of conservation relations, resulting in better out-of-sample generalization. Additionally, PAPM contains a holistic temporal-spatial stepping module for flexible adaptation across various process systems. Through systematic comparisons with state-of-the-art pure data-driven and physics-aware models across five two-dimensional benchmarks in nine generalization tasks, PAPM notably achieves an average performance improvement of 6.7%, while requiring fewer FLOPs, and just 1% of the parameters compared to the prior leading method. The code is available at https://github.com/pengwei07/PAPM.
|
http://arxiv.org/pdf/2407.05232v1
|
[
"Pengwei Liu",
"Zhongkai Hao",
"Xingyu Ren",
"Hangjie Yuan",
"Jiayang Ren",
"Dong Ni"
] |
2024-07-07T02:10:05Z
|
2024-07-07T02:10:05Z
|
2406.18931
|
Semi-adaptive Synergetic Two-way Pseudoinverse Learning System
|
Deep learning has become a crucial technology for making breakthroughs in many fields. Nevertheless, it still faces two important challenges in theoretical and applied aspects. The first lies in the shortcomings of gradient descent based learning schemes which are time-consuming and difficult to determine the learning control hyperparameters. Next, the architectural design of the model is usually tricky. In this paper, we propose a semi-adaptive synergetic two-way pseudoinverse learning system, wherein each subsystem encompasses forward learning, backward learning, and feature concatenation modules. The whole system is trained using a non-gradient descent learning algorithm. It simplifies the hyperparameter tuning while improving the training efficiency. The architecture of the subsystems is designed using a data-driven approach that enables automated determination of the depth of the subsystems. We compare our method with the baselines of mainstream non-gradient descent based methods and the results demonstrate the effectiveness of our proposed method. The source code for this paper is available at http://github.com/B-berrypie/Semi-adaptive-Synergetic-Two-way-Pseudoinverse-Learning-System}{http://github.com/B-berrypie/Semi-adaptive-Synergetic-Two-way-Pseudoinverse-Learning-System.
|
http://arxiv.org/pdf/2406.18931v2
|
[
"Binghong Liu",
"Ziqi Zhao",
"Shupan Li",
"Ke Wang"
] |
2024-07-07T02:02:44Z
|
2024-06-27T06:56:46Z
|
2407.05229
|
HiDe-PET: Continual Learning via Hierarchical Decomposition of
Parameter-Efficient Tuning
|
The deployment of pre-trained models (PTMs) has greatly advanced the field of continual learning (CL), enabling positive knowledge transfer and resilience to catastrophic forgetting. To sustain these advantages for sequentially arriving tasks, a promising direction involves keeping the pre-trained backbone frozen while employing parameter-efficient tuning (PET) techniques to instruct representation learning. Despite the popularity of Prompt-based PET for CL, its empirical design often leads to sub-optimal performance in our evaluation of different PTMs and target tasks. To this end, we propose a unified framework for CL with PTMs and PET that provides both theoretical and empirical advancements. We first perform an in-depth theoretical analysis of the CL objective in a pre-training context, decomposing it into hierarchical components namely within-task prediction, task-identity inference and task-adaptive prediction. We then present Hierarchical Decomposition PET (HiDe-PET), an innovative approach that explicitly optimizes the decomposed objective through incorporating task-specific and task-shared knowledge via mainstream PET techniques along with efficient recovery of pre-trained representations. Leveraging this framework, we delve into the distinct impacts of implementation strategy, PET technique and PET architecture, as well as adaptive knowledge accumulation amidst pronounced distribution changes. Finally, across various CL scenarios, our approach demonstrates remarkably superior performance over a broad spectrum of recent strong baselines.
|
http://arxiv.org/pdf/2407.05229v1
|
[
"Liyuan Wang",
"Jingyi Xie",
"Xingxing Zhang",
"Hang Su",
"Jun Zhu"
] |
2024-07-07T01:50:25Z
|
2024-07-07T01:50:25Z
|
2407.05224
|
On the importance of learning non-local dynamics for stable data-driven
climate modeling: A 1D gravity wave-QBO testbed
|
Machine learning (ML) techniques, especially neural networks (NNs), have shown promise in learning subgrid-scale (SGS) parameterizations for climate modeling. However, a major problem with data-driven parameterizations, particularly those learned with supervised algorithms, is instability when integrated with numerical solvers of large-scale processes. Current remedies are often ad-hoc and lack a theoretical foundation. Here, we combine ML theory and climate physics to address a source of instability in NN-based parameterization. We demonstrate the importance of learning spatially non-local dynamics using a 1D model of the quasi-biennial oscillation (QBO) with gravity wave (GW) parameterization as a testbed. While common offline metrics fail to identify shortcomings in learning non-local dynamics, we show that the receptive field (RF)-the region of the input an NN uses to predict an output-can identify instability a-priori. We find that NN-based parameterizations that seem to accurately predict GW forcings from wind profiles ($mathbf{R^2 approx 0.99}$) cause unstable simulations when RF is too small to capture the non-local dynamics, while NNs of the same size but large-enough RF are stable. Some architectures, e.g., Fourier neural operators, have inherently large RF. We also demonstrate that learning non-local dynamics can be crucial for the stability and accuracy of a data-driven spatiotemporal emulator of the entire zonal wind field. Given the ubiquity of non-local dynamics in the climate system, we expect the use of effective RF, which can be computed for any NN architecture, to be important for many applications. This work highlights the need to integrate ML theory with physics for designing/analyzing data-driven algorithms for weather/climate modeling.
|
http://arxiv.org/pdf/2407.05224v1
|
[
"Hamid A. Pahlavan",
"Pedram Hassanzadeh",
"M. Joan Alexander"
] |
2024-07-07T01:15:52Z
|
2024-07-07T01:15:52Z
|
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