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2407.03925
|
Reduced-Order Neural Operators: Learning Lagrangian Dynamics on Highly
Sparse Graphs
|
We present a neural operator architecture to simulate Lagrangian dynamics, such as fluid flow, granular flows, and elastoplasticity. Traditional numerical methods, such as the finite element method (FEM), suffer from long run times and large memory consumption. On the other hand, approaches based on graph neural networks are faster but still suffer from long computation times on dense graphs, which are often required for high-fidelity simulations. Our model, GIOROM or Graph Interaction Operator for Reduced-Order Modeling, learns temporal dynamics within a reduced-order setting, capturing spatial features from a highly sparse graph representation of the input and generalizing to arbitrary spatial locations during inference. The model is geometry-aware and discretization-agnostic and can generalize to different initial conditions, velocities, and geometries after training. We show that point clouds of the order of 100,000 points can be inferred from sparse graphs with $sim$1000 points, with negligible change in computation time. We empirically evaluate our model on elastic solids, Newtonian fluids, Non-Newtonian fluids, Drucker-Prager granular flows, and von Mises elastoplasticity. On these benchmarks, our approach results in a 25$times$ speedup compared to other neural network-based physics simulators while delivering high-fidelity predictions of complex physical systems and showing better performance on most benchmarks. The code and the demos are provided at https://github.com/HrishikeshVish/GIOROM.
|
http://arxiv.org/pdf/2407.03925v1
|
[
"Hrishikesh Viswanath",
"Yue Chang",
"Julius Berner",
"Peter Yichen Chen",
"Aniket Bera"
] |
2024-07-04T13:37:26Z
|
2024-07-04T13:37:26Z
|
2407.03921
|
Concept Bottleneck Models Without Predefined Concepts
|
There has been considerable recent interest in interpretable concept-based models such as Concept Bottleneck Models (CBMs), which first predict human-interpretable concepts and then map them to output classes. To reduce reliance on human-annotated concepts, recent works have converted pretrained black-box models into interpretable CBMs post-hoc. However, these approaches predefine a set of concepts, assuming which concepts a black-box model encodes in its representations. In this work, we eliminate this assumption by leveraging unsupervised concept discovery to automatically extract concepts without human annotations or a predefined set of concepts. We further introduce an input-dependent concept selection mechanism that ensures only a small subset of concepts is used across all classes. We show that our approach improves downstream performance and narrows the performance gap to black-box models, while using significantly fewer concepts in the classification. Finally, we demonstrate how large vision-language models can intervene on the final model weights to correct model errors.
|
http://arxiv.org/pdf/2407.03921v1
|
[
"Simon Schrodi",
"Julian Schur",
"Max Argus",
"Thomas Brox"
] |
2024-07-04T13:34:50Z
|
2024-07-04T13:34:50Z
|
2407.03920
|
Support Vector Based Anomaly Detection in Federated Learning
|
Anomaly detection plays a crucial role in various domains, from cybersecurity to industrial systems. However, traditional centralized approaches often encounter challenges related to data privacy. In this context, Federated Learning emerges as a promising solution. This work introduces two innovative algorithms--Ensemble SVDD and Support Vector Election--that leverage Support Vector Machines for anomaly detection in a federated setting. In comparison with the Neural Networks typically used in within Federated Learning, these new algorithms emerge as potential alternatives, as they can operate effectively with small datasets and incur lower computational costs. The novel algorithms are tested in various distributed system configurations, yielding promising initial results that pave the way for further investigation.
|
http://arxiv.org/pdf/2407.03920v1
|
[
"Massimo Frasson",
"Dario Malchiodi"
] |
2024-07-04T13:32:17Z
|
2024-07-04T13:32:17Z
|
2401.10545
|
Understanding Biases in ChatGPT-based Recommender Systems: Provider
Fairness, Temporal Stability, and Recency
|
This paper explores the biases in ChatGPT-based recommender systems, focusing on provider fairness (item-side fairness). Through extensive experiments and over a thousand API calls, we investigate the impact of prompt design strategies-including structure, system role, and intent-on evaluation metrics such as provider fairness, catalog coverage, temporal stability, and recency. The first experiment examines these strategies in classical top-K recommendations, while the second evaluates sequential in-context learning (ICL). In the first experiment, we assess seven distinct prompt scenarios on top-K recommendation accuracy and fairness. Accuracy-oriented prompts, like Simple and Chain-of-Thought (COT), outperform diversification prompts, which, despite enhancing temporal freshness, reduce accuracy by up to 50%. Embedding fairness into system roles, such as "act as a fair recommender," proved more effective than fairness directives within prompts. Diversification prompts led to recommending newer movies, offering broader genre distribution compared to traditional collaborative filtering (CF) models. The second experiment explores sequential ICL, comparing zero-shot and few-shot ICL. Results indicate that including user demographic information in prompts affects model biases and stereotypes. However, ICL did not consistently improve item fairness and catalog coverage over zero-shot learning. Zero-shot learning achieved higher NDCG and coverage, while ICL-2 showed slight improvements in hit rate (HR) when age-group context was included. Our study provides insights into biases of RecLLMs, particularly in provider fairness and catalog coverage. By examining prompt design, learning strategies, and system roles, we highlight the potential and challenges of integrating LLMs into recommendation systems. Further details can be found at https://github.com/yasdel/Benchmark_RecLLM_Fairness.
|
http://arxiv.org/pdf/2401.10545v3
|
[
"Yashar Deldjoo"
] |
2024-07-04T12:59:01Z
|
2024-01-19T08:09:20Z
|
2306.00427
|
Out-of-distribution forgetting: vulnerability of continual learning to
intra-class distribution shift
|
Continual learning (CL) is an important technique to allow artificial neural networks to work in open environments. CL enables a system to learn new tasks without severe interference to its performance on old tasks, i.e., overcome the problems of catastrophic forgetting. In joint learning, it is well known that the out-of-distribution (OOD) problem caused by intentional attacks or environmental perturbations will severely impair the ability of networks to generalize. In this work, we reported a special form of catastrophic forgetting raised by the OOD problem in continual learning settings, and we named it out-of-distribution forgetting (OODF). In continual image classification tasks, we found that for a given category, introducing an intra-class distribution shift significantly impaired the recognition accuracy of CL methods for that category during subsequent learning. Interestingly, this phenomenon is special for CL as the same level of distribution shift had only negligible effects in the joint learning scenario. We verified that CL methods without dedicating subnetworks for individual tasks are all vulnerable to OODF. Moreover, OODF does not depend on any specific way of shifting the distribution, suggesting it is a risk for CL in a wide range of circumstances. Taken together, our work identified an under-attended risk during CL, highlighting the importance of developing approaches that can overcome OODF. Code available: url{https://github.com/Hiroid/OODF}
|
http://arxiv.org/pdf/2306.00427v2
|
[
"Liangxuan Guo",
"Yang Chen",
"Shan Yu"
] |
2024-07-04T12:56:44Z
|
2023-06-01T08:07:58Z
|
2407.03901
|
DiCTI: Diffusion-based Clothing Designer via Text-guided Input
|
Recent developments in deep generative models have opened up a wide range of opportunities for image synthesis, leading to significant changes in various creative fields, including the fashion industry. While numerous methods have been proposed to benefit buyers, particularly in virtual try-on applications, there has been relatively less focus on facilitating fast prototyping for designers and customers seeking to order new designs. To address this gap, we introduce DiCTI (Diffusion-based Clothing Designer via Text-guided Input), a straightforward yet highly effective approach that allows designers to quickly visualize fashion-related ideas using text inputs only. Given an image of a person and a description of the desired garments as input, DiCTI automatically generates multiple high-resolution, photorealistic images that capture the expressed semantics. By leveraging a powerful diffusion-based inpainting model conditioned on text inputs, DiCTI is able to synthesize convincing, high-quality images with varied clothing designs that viably follow the provided text descriptions, while being able to process very diverse and challenging inputs, captured in completely unconstrained settings. We evaluate DiCTI in comprehensive experiments on two different datasets (VITON-HD and Fashionpedia) and in comparison to the state-of-the-art (SoTa). The results of our experiments show that DiCTI convincingly outperforms the SoTA competitor in generating higher quality images with more elaborate garments and superior text prompt adherence, both according to standard quantitative evaluation measures and human ratings, generated as part of a user study.
|
http://arxiv.org/pdf/2407.03901v1
|
[
"Ajda Lampe",
"Julija Stopar",
"Deepak Kumar Jain",
"Shinichiro Omachi",
"Peter Peer",
"Vitomir Štruc"
] |
2024-07-04T12:48:36Z
|
2024-07-04T12:48:36Z
|
2407.03897
|
gFlora: a topology-aware method to discover functional co-response
groups in soil microbial communities
|
We aim to learn the functional co-response group: a group of taxa whose co-response effect (the representative characteristic of the group) associates well statistically with a functional variable. Different from the state-of-the-art method, we model the soil microbial community as an ecological co-occurrence network with the taxa as nodes (weighted by their abundance) and their relationships (a combination from both spatial and functional ecological aspects) as edges (weighted by the strength of the relationships). Then, we design a method called gFlora which notably uses graph convolution over this co-occurrence network to get the co-response effect of the group, such that the network topology is also considered in the discovery process. We evaluate gFlora on two real-world soil microbiome datasets (bacteria and nematodes) and compare it with the state-of-the-art method. gFlora outperforms this on all evaluation metrics, and discovers new functional evidence for taxa which were so far under-studied. We show that the graph convolution step is crucial to taxa with low abundance, and the discovered bacteria of different genera are distributed in the co-occurrence network but still tightly connected among themselves, demonstrating that topologically they fill different but collaborative functional roles in the ecological community.
|
http://arxiv.org/pdf/2407.03897v1
|
[
"Nan Chen",
"Merlijn Schram",
"Doina Bucur"
] |
2024-07-04T12:43:53Z
|
2024-07-04T12:43:53Z
|
2407.02070
|
Latent Diffusion Model for Generating Ensembles of Climate Simulations
|
Obtaining accurate estimates of uncertainty in climate scenarios often requires generating large ensembles of high-resolution climate simulations, a computationally expensive and memory intensive process. To address this challenge, we train a novel generative deep learning approach on extensive sets of climate simulations. The model consists of two components: a variational autoencoder for dimensionality reduction and a denoising diffusion probabilistic model that generates multiple ensemble members. We validate our model on the Max Planck Institute Grand Ensemble and show that it achieves good agreement with the original ensemble in terms of variability. By leveraging the latent space representation, our model can rapidly generate large ensembles on-the-fly with minimal memory requirements, which can significantly improve the efficiency of uncertainty quantification in climate simulations.
|
http://arxiv.org/pdf/2407.02070v2
|
[
"Johannes Meuer",
"Maximilian Witte",
"Tobias Sebastian Finn",
"Claudia Timmreck",
"Thomas Ludwig",
"Christopher Kadow"
] |
2024-07-04T12:43:52Z
|
2024-07-02T08:59:24Z
|
2210.10849
|
Black Box Model Explanations and the Human Interpretability Expectations
-- An Analysis in the Context of Homicide Prediction
|
Strategies based on Explainable Artificial Intelligence (XAI) have promoted better human interpretability of the results of black box models. This opens up the possibility of questioning whether explanations created by XAI methods meet human expectations. The XAI methods being currently used (Ciu, Dalex, Eli5, Lofo, Shap, and Skater) provide various forms of explanations, including global rankings of relevance of features, which allow for an overview of how the model is explained as a result of its inputs and outputs. These methods provide for an increase in the explainability of the model and a greater interpretability grounded on the context of the problem. Intending to shed light on the explanations generated by XAI methods and their interpretations, this research addresses a real-world classification problem related to homicide prediction, already peer-validated, replicated its proposed black box model and used 6 different XAI methods to generate explanations and 6 different human experts. The results were generated through calculations of correlations, comparative analysis and identification of relationships between all ranks of features produced. It was found that even though it is a model that is difficult to explain, 75% of the expectations of human experts were met, with approximately 48% agreement between results from XAI methods and human experts. The results allow for answering questions such as: "Are the Expectation of Interpretation generated among different human experts similar?", "Do the different XAI methods generate similar explanations for the proposed problem?", "Can explanations generated by XAI methods meet human expectation of Interpretations?", and "Can Explanations and Expectations of Interpretation work together?".
|
http://arxiv.org/pdf/2210.10849v2
|
[
"José Ribeiro",
"Níkolas Carneiro",
"Ronnie Alves"
] |
2024-07-04T12:39:08Z
|
2022-10-19T19:23:48Z
|
2308.11038
|
Logistics Hub Location Optimization: A K-Means and P-Median Model Hybrid
Approach Using Road Network Distances
|
Logistic hubs play a pivotal role in the last-mile delivery distance; even a slight increment in distance negatively impacts the business of the e-commerce industry while also increasing its carbon footprint. The growth of this industry, particularly after Covid-19, has further intensified the need for optimized allocation of resources in an urban environment. In this study, we use a hybrid approach to optimize the placement of logistic hubs. The approach sequentially employs different techniques. Initially, delivery points are clustered using K-Means in relation to their spatial locations. The clustering method utilizes road network distances as opposed to Euclidean distances. Non-road network-based approaches have been avoided since they lead to erroneous and misleading results. Finally, hubs are located using the P-Median method. The P-Median method also incorporates the number of deliveries and population as weights. Real-world delivery data from Muller and Phipps (M&P) is used to demonstrate the effectiveness of the approach. Serving deliveries from the optimal hub locations results in the saving of 815 (10%) meters per delivery.
|
http://arxiv.org/pdf/2308.11038v3
|
[
"Muhammad Abdul Rahman",
"Muhammad Aamir Basheer",
"Zubair Khalid",
"Muhammad Tahir",
"Momin Uppal"
] |
2024-07-04T12:36:48Z
|
2023-08-18T10:28:07Z
|
2407.03888
|
Continuous-time q-Learning for Jump-Diffusion Models under Tsallis
Entropy
|
This paper studies continuous-time reinforcement learning for controlled jump-diffusion models by featuring the q-function (the continuous-time counterpart of Q-function) and the q-learning algorithms under the Tsallis entropy regularization. Contrary to the conventional Shannon entropy, the general form of Tsallis entropy renders the optimal policy not necessary a Gibbs measure, where some Lagrange multiplier and KKT multiplier naturally arise from certain constraints to ensure the learnt policy to be a probability distribution. As a consequence,the relationship between the optimal policy and the q-function also involves the Lagrange multiplier. In response, we establish the martingale characterization of the q-function under Tsallis entropy and devise two q-learning algorithms depending on whether the Lagrange multiplier can be derived explicitly or not. In the latter case, we need to consider different parameterizations of the q-function and the policy and update them alternatively. Finally, we examine two financial applications, namely an optimal portfolio liquidation problem and a non-LQ control problem. It is interesting to see therein that the optimal policies under the Tsallis entropy regularization can be characterized explicitly, which are distributions concentrate on some compact support. The satisfactory performance of our q-learning algorithm is illustrated in both examples.
|
http://arxiv.org/pdf/2407.03888v1
|
[
"Lijun Bo",
"Yijie Huang",
"Xiang Yu",
"Tingting Zhang"
] |
2024-07-04T12:26:31Z
|
2024-07-04T12:26:31Z
|
2407.03883
|
Protecting Deep Learning Model Copyrights with Adversarial Example-Free
Reuse Detection
|
Model reuse techniques can reduce the resource requirements for training high-performance deep neural networks (DNNs) by leveraging existing models. However, unauthorized reuse and replication of DNNs can lead to copyright infringement and economic loss to the model owner. This underscores the need to analyze the reuse relation between DNNs and develop copyright protection techniques to safeguard intellectual property rights. Existing white-box testing-based approaches cannot address the common heterogeneous reuse case where the model architecture is changed, and DNN fingerprinting approaches heavily rely on generating adversarial examples with good transferability, which is known to be challenging in the black-box setting. To bridge the gap, we propose NFARD, a Neuron Functionality Analysis-based Reuse Detector, which only requires normal test samples to detect reuse relations by measuring the models' differences on a newly proposed model characterization, i.e., neuron functionality (NF). A set of NF-based distance metrics is designed to make NFARD applicable to both white-box and black-box settings. Moreover, we devise a linear transformation method to handle heterogeneous reuse cases by constructing the optimal projection matrix for dimension consistency, significantly extending the application scope of NFARD. To the best of our knowledge, this is the first adversarial example-free method that exploits neuron functionality for DNN copyright protection. As a side contribution, we constructed a reuse detection benchmark named Reuse Zoo that covers various practical reuse techniques and popular datasets. Extensive evaluations on this comprehensive benchmark show that NFARD achieves F1 scores of 0.984 and 1.0 for detecting reuse relationships in black-box and white-box settings, respectively, while generating test suites 2 ~ 99 times faster than previous methods.
|
http://arxiv.org/pdf/2407.03883v1
|
[
"Xiaokun Luan",
"Xiyue Zhang",
"Jingyi Wang",
"Meng Sun"
] |
2024-07-04T12:21:59Z
|
2024-07-04T12:21:59Z
|
2407.03878
|
Geodesic Optimization for Predictive Shift Adaptation on EEG data
|
Electroencephalography (EEG) data is often collected from diverse contexts involving different populations and EEG devices. This variability can induce distribution shifts in the data $X$ and in the biomedical variables of interest $y$, thus limiting the application of supervised machine learning (ML) algorithms. While domain adaptation (DA) methods have been developed to mitigate the impact of these shifts, such methods struggle when distribution shifts occur simultaneously in $X$ and $y$. As state-of-the-art ML models for EEG represent the data by spatial covariance matrices, which lie on the Riemannian manifold of Symmetric Positive Definite (SPD) matrices, it is appealing to study DA techniques operating on the SPD manifold. This paper proposes a novel method termed Geodesic Optimization for Predictive Shift Adaptation (GOPSA) to address test-time multi-source DA for situations in which source domains have distinct $y$ distributions. GOPSA exploits the geodesic structure of the Riemannian manifold to jointly learn a domain-specific re-centering operator representing site-specific intercepts and the regression model. We performed empirical benchmarks on the cross-site generalization of age-prediction models with resting-state EEG data from a large multi-national dataset (HarMNqEEG), which included $14$ recording sites and more than $1500$ human participants. Compared to state-of-the-art methods, our results showed that GOPSA achieved significantly higher performance on three regression metrics ($R^2$, MAE, and Spearman's $rho$) for several source-target site combinations, highlighting its effectiveness in tackling multi-source DA with predictive shifts in EEG data analysis. Our method has the potential to combine the advantages of mixed-effects modeling with machine learning for biomedical applications of EEG, such as multicenter clinical trials.
|
http://arxiv.org/pdf/2407.03878v1
|
[
"Apolline Mellot",
"Antoine Collas",
"Sylvain Chevallier",
"Alexandre Gramfort",
"Denis A. Engemann"
] |
2024-07-04T12:15:42Z
|
2024-07-04T12:15:42Z
|
2407.03872
|
The Solution for the GAIIC2024 RGB-TIR object detection Challenge
|
This report introduces a solution to The task of RGB-TIR object detection from the perspective of unmanned aerial vehicles. Unlike traditional object detection methods, RGB-TIR object detection aims to utilize both RGB and TIR images for complementary information during detection. The challenges of RGB-TIR object detection from the perspective of unmanned aerial vehicles include highly complex image backgrounds, frequent changes in lighting, and uncalibrated RGB-TIR image pairs. To address these challenges at the model level, we utilized a lightweight YOLOv9 model with extended multi-level auxiliary branches that enhance the model's robustness, making it more suitable for practical applications in unmanned aerial vehicle scenarios. For image fusion in RGB-TIR detection, we incorporated a fusion module into the backbone network to fuse images at the feature level, implicitly addressing calibration issues. Our proposed method achieved an mAP score of 0.516 and 0.543 on A and B benchmarks respectively while maintaining the highest inference speed among all models.
|
http://arxiv.org/pdf/2407.03872v1
|
[
"Xiangyu Wu",
"Jinling Xu",
"Longfei Huang",
"Yang Yang"
] |
2024-07-04T12:08:36Z
|
2024-07-04T12:08:36Z
|
2407.03864
|
Adversarial Robustness of VAEs across Intersectional Subgroups
|
Despite advancements in Autoencoders (AEs) for tasks like dimensionality reduction, representation learning and data generation, they remain vulnerable to adversarial attacks. Variational Autoencoders (VAEs), with their probabilistic approach to disentangling latent spaces, show stronger resistance to such perturbations compared to deterministic AEs; however, their resilience against adversarial inputs is still a concern. This study evaluates the robustness of VAEs against non-targeted adversarial attacks by optimizing minimal sample-specific perturbations to cause maximal damage across diverse demographic subgroups (combinations of age and gender). We investigate two questions: whether there are robustness disparities among subgroups, and what factors contribute to these disparities, such as data scarcity and representation entanglement. Our findings reveal that robustness disparities exist but are not always correlated with the size of the subgroup. By using downstream gender and age classifiers and examining latent embeddings, we highlight the vulnerability of subgroups like older women, who are prone to misclassification due to adversarial perturbations pushing their representations toward those of other subgroups.
|
http://arxiv.org/pdf/2407.03864v1
|
[
"Chethan Krishnamurthy Ramanaik",
"Arjun Roy",
"Eirini Ntoutsi"
] |
2024-07-04T11:53:51Z
|
2024-07-04T11:53:51Z
|
2407.03862
|
FedSat: A Statistical Aggregation Approach for Class Imbalaced Clients
in Federated Learning
|
Federated learning (FL) has emerged as a promising paradigm for privacy-preserving distributed machine learning, but faces challenges with heterogeneous data distributions across clients. This paper introduces FedSat, a novel FL approach designed to tackle various forms of data heterogeneity simultaneously. FedSat employs a cost-sensitive loss function and a prioritized class-based weighted aggregation scheme to address label skewness, missing classes, and quantity skewness across clients. While the proposed cost-sensitive loss function enhances model performance on minority classes, the prioritized class-based weighted aggregation scheme ensures client contributions are weighted based on both statistical significance and performance on critical classes. Extensive experiments across diverse data-heterogeneity settings demonstrate that FedSat significantly outperforms state-of-the-art baselines, with an average improvement of 1.8% over the second-best method and 19.87% over the weakest-performing baseline. The approach also demonstrates faster convergence compared to existing methods. These results highlight FedSat's effectiveness in addressing the challenges of heterogeneous federated learning and its potential for real-world applications.
|
http://arxiv.org/pdf/2407.03862v1
|
[
"Sujit Chowdhury",
"Raju Halder"
] |
2024-07-04T11:50:24Z
|
2024-07-04T11:50:24Z
|
2407.03856
|
Q-Adapter: Training Your LLM Adapter as a Residual Q-Function
|
We consider the problem of adapting Large Language Models (LLMs) pre-trained with Reinforcement Learning from Human Feedback (RLHF) to downstream preference data. Naive approaches to achieve this could be supervised fine-tuning on preferred responses or reinforcement learning with a learned reward model. However, the LLM runs the risk of forgetting its initial knowledge as the fine-tuning progresses. To customize the LLM while preserving its existing capabilities, this paper proposes a novel method, named as Q-Adapter. We start by formalizing LLM adaptation as a problem of maximizing the linear combination of two rewards, one of which corresponds to the reward optimized by the pre-trained LLM and the other to the downstream preference data. Although both rewards are unknown, we show that this can be solved by directly learning a new module from the preference data that approximates the emph{residual Q-function}. We consider this module to be an adapter because the original pre-trained LLM, together with it, can form the optimal customised LLM. Empirically, experiments on a range of domain-specific tasks and safety alignment tasks illustrate the superiority of Q-Adapter in both anti-forgetting and learning from new preferences.
|
http://arxiv.org/pdf/2407.03856v1
|
[
"Yi-Chen Li",
"Fuxiang Zhang",
"Wenjie Qiu",
"Lei Yuan",
"Chengxing Jia",
"Zongzhang Zhang",
"Yang Yu"
] |
2024-07-04T11:42:36Z
|
2024-07-04T11:42:36Z
|
2303.11602
|
Convergence of variational Monte Carlo simulation and scale-invariant
pre-training
|
We provide theoretical convergence bounds for the variational Monte Carlo (VMC) method as applied to optimize neural network wave functions for the electronic structure problem. We study both the energy minimization phase and the supervised pre-training phase that is commonly used prior to energy minimization. For the energy minimization phase, the standard algorithm is scale-invariant by design, and we provide a proof of convergence for this algorithm without modifications. The pre-training stage typically does not feature such scale-invariance. We propose using a scale-invariant loss for the pretraining phase and demonstrate empirically that it leads to faster pre-training.
|
http://arxiv.org/pdf/2303.11602v4
|
[
"Nilin Abrahamsen",
"Zhiyan Ding",
"Gil Goldshlager",
"Lin Lin"
] |
2024-07-04T11:39:00Z
|
2023-03-21T05:41:24Z
|
2407.03852
|
Low-latency machine learning FPGA accelerator for multi-qubit state
discrimination
|
Measuring a qubit is a fundamental yet error prone operation in quantum computing. These errors can stem from various sources such as crosstalk, spontaneous state-transitions, and excitation caused by the readout pulse. In this work, we utilize an integrated approach to deploy neural networks (NN) on to field programmable gate arrays (FPGA). We demonstrate that it is practical to design and implement a fully connected neural network accelerator for frequency-multiplexed readout balancing computational complexity with low latency requirements without significant loss in accuracy. The neural network is implemented by quantization of weights, activation functions, and inputs. The hardware accelerator performs frequency-multiplexed readout of 5 superconducting qubits in less than 50 ns on RFSoC ZCU111 FPGA which is first of its kind in the literature. These modules can be implemented and integrated in existing Quantum control and readout platforms using a RFSoC ZCU111 ready for experimental deployment.
|
http://arxiv.org/pdf/2407.03852v1
|
[
"Pradeep Kumar Gautam",
"Shantharam Kalipatnapu",
"Shankaranarayanan H",
"Ujjawal Singhal",
"Benjamin Lienhard",
"Vibhor Singh",
"Chetan Singh Thakur"
] |
2024-07-04T11:34:43Z
|
2024-07-04T11:34:43Z
|
2407.03851
|
Implicit Hypersurface Approximation Capacity in Deep ReLU Networks
|
We develop a geometric approximation theory for deep feed-forward neural networks with ReLU activations. Given a $d$-dimensional hypersurface in $mathbb{R}^{d+1}$ represented as the graph of a $C^2$-function $phi$, we show that a deep fully-connected ReLU network of width $d+1$ can implicitly construct an approximation as its zero contour with a precision bound depending on the number of layers. This result is directly applicable to the binary classification setting where the sign of the network is trained as a classifier, with the network's zero contour as a decision boundary. Our proof is constructive and relies on the geometrical structure of ReLU layers provided in [doi:10.48550/arXiv.2310.03482]. Inspired by this geometrical description, we define a new equivalent network architecture that is easier to interpret geometrically, where the action of each hidden layer is a projection onto a polyhedral cone derived from the layer's parameters. By repeatedly adding such layers, with parameters chosen such that we project small parts of the graph of $phi$ from the outside in, we, in a controlled way, construct a network that implicitly approximates the graph over a ball of radius $R$. The accuracy of this construction is controlled by a discretization parameter $delta$ and we show that the tolerance in the resulting error bound scales as $(d-1)R^{3/2}delta^{1/2}$ and the required number of layers is of order $dbig(frac{32R}{delta}big)^{frac{d+1}{2}}$.
|
http://arxiv.org/pdf/2407.03851v1
|
[
"Jonatan Vallin",
"Karl Larsson",
"Mats G. Larson"
] |
2024-07-04T11:34:42Z
|
2024-07-04T11:34:42Z
|
2407.03848
|
Bias of Stochastic Gradient Descent or the Architecture: Disentangling
the Effects of Overparameterization of Neural Networks
|
Neural networks typically generalize well when fitting the data perfectly, even though they are heavily overparameterized. Many factors have been pointed out as the reason for this phenomenon, including an implicit bias of stochastic gradient descent (SGD) and a possible simplicity bias arising from the neural network architecture. The goal of this paper is to disentangle the factors that influence generalization stemming from optimization and architectural choices by studying random and SGD-optimized networks that achieve zero training error. We experimentally show, in the low sample regime, that overparameterization in terms of increasing width is beneficial for generalization, and this benefit is due to the bias of SGD and not due to an architectural bias. In contrast, for increasing depth, overparameterization is detrimental for generalization, but random and SGD-optimized networks behave similarly, so this can be attributed to an architectural bias. For more information, see https://bias-sgd-or-architecture.github.io .
|
http://arxiv.org/pdf/2407.03848v1
|
[
"Amit Peleg",
"Matthias Hein"
] |
2024-07-04T11:29:50Z
|
2024-07-04T11:29:50Z
|
2206.01011
|
Policy Gradient Algorithms with Monte Carlo Tree Learning for Non-Markov
Decision Processes
|
Policy gradient (PG) is a reinforcement learning (RL) approach that optimizes a parameterized policy model for an expected return using gradient ascent. While PG can work well even in non-Markovian environments, it may encounter plateaus or peakiness issues. As another successful RL approach, algorithms based on Monte Carlo Tree Search (MCTS), which include AlphaZero, have obtained groundbreaking results, especially in the game-playing domain. They are also effective when applied to non-Markov decision processes. However, the standard MCTS is a method for decision-time planning, which differs from the online RL setting. In this work, we first introduce Monte Carlo Tree Learning (MCTL), an adaptation of MCTS for online RL setups. We then explore a combined policy approach of PG and MCTL to leverage their strengths. We derive conditions for asymptotic convergence with the results of a two-timescale stochastic approximation and propose an algorithm that satisfies these conditions and converges to a reasonable solution. Our numerical experiments validate the effectiveness of the proposed methods.
|
http://arxiv.org/pdf/2206.01011v2
|
[
"Tetsuro Morimura",
"Kazuhiro Ota",
"Kenshi Abe",
"Peinan Zhang"
] |
2024-07-04T11:22:01Z
|
2022-06-02T12:21:40Z
|
2402.15739
|
Low-Rank Bandits via Tight Two-to-Infinity Singular Subspace Recovery
|
We study contextual bandits with low-rank structure where, in each round, if the (context, arm) pair $(i,j)in [m]times [n]$ is selected, the learner observes a noisy sample of the $(i,j)$-th entry of an unknown low-rank reward matrix. Successive contexts are generated randomly in an i.i.d. manner and are revealed to the learner. For such bandits, we present efficient algorithms for policy evaluation, best policy identification and regret minimization. For policy evaluation and best policy identification, we show that our algorithms are nearly minimax optimal. For instance, the number of samples required to return an $varepsilon$-optimal policy with probability at least $1-delta$ typically scales as ${r(m+n)over varepsilon^2}log(1/delta)$. Our regret minimization algorithm enjoys minimax guarantees typically scaling as $r^{7/4}(m+n)^{3/4}sqrt{T}$, which improves over existing algorithms. All the proposed algorithms consist of two phases: they first leverage spectral methods to estimate the left and right singular subspaces of the low-rank reward matrix. We show that these estimates enjoy tight error guarantees in the two-to-infinity norm. This in turn allows us to reformulate our problems as a misspecified linear bandit problem with dimension roughly $r(m+n)$ and misspecification controlled by the subspace recovery error, as well as to design the second phase of our algorithms efficiently.
|
http://arxiv.org/pdf/2402.15739v2
|
[
"Yassir Jedra",
"William Réveillard",
"Stefan Stojanovic",
"Alexandre Proutiere"
] |
2024-07-04T11:17:38Z
|
2024-02-24T06:36:08Z
|
2407.03836
|
ADAPT: Multimodal Learning for Detecting Physiological Changes under
Missing Modalities
|
Multimodality has recently gained attention in the medical domain, where imaging or video modalities may be integrated with biomedical signals or health records. Yet, two challenges remain: balancing the contributions of modalities, especially in cases with a limited amount of data available, and tackling missing modalities. To address both issues, in this paper, we introduce the AnchoreD multimodAl Physiological Transformer (ADAPT), a multimodal, scalable framework with two key components: (i) aligning all modalities in the space of the strongest, richest modality (called anchor) to learn a joint embedding space, and (ii) a Masked Multimodal Transformer, leveraging both inter- and intra-modality correlations while handling missing modalities. We focus on detecting physiological changes in two real-life scenarios: stress in individuals induced by specific triggers and fighter pilots' loss of consciousness induced by $g$-forces. We validate the generalizability of ADAPT through extensive experiments on two datasets for these tasks, where we set the new state of the art while demonstrating its robustness across various modality scenarios and its high potential for real-life applications.
|
http://arxiv.org/pdf/2407.03836v1
|
[
"Julie Mordacq",
"Leo Milecki",
"Maria Vakalopoulou",
"Steve Oudot",
"Vicky Kalogeiton"
] |
2024-07-04T11:05:14Z
|
2024-07-04T11:05:14Z
|
2407.03834
|
10 Years of Fair Representations: Challenges and Opportunities
|
Fair Representation Learning (FRL) is a broad set of techniques, mostly based on neural networks, that seeks to learn new representations of data in which sensitive or undesired information has been removed. Methodologically, FRL was pioneered by Richard Zemel et al. about ten years ago. The basic concepts, objectives and evaluation strategies for FRL methodologies remain unchanged to this day. In this paper, we look back at the first ten years of FRL by i) revisiting its theoretical standing in light of recent work in deep learning theory that shows the hardness of removing information in neural network representations and ii) presenting the results of a massive experimentation (225.000 model fits and 110.000 AutoML fits) we conducted with the objective of improving on the common evaluation scenario for FRL. More specifically, we use automated machine learning (AutoML) to adversarially "mine" sensitive information from supposedly fair representations. Our theoretical and experimental analysis suggests that deterministic, unquantized FRL methodologies have serious issues in removing sensitive information, which is especially troubling as they might seem "fair" at first glance.
|
http://arxiv.org/pdf/2407.03834v1
|
[
"Mattia Cerrato",
"Marius Köppel",
"Philipp Wolf",
"Stefan Kramer"
] |
2024-07-04T11:04:26Z
|
2024-07-04T11:04:26Z
|
2406.02318
|
PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly
Detection
|
With the proliferation of mobile sensing techniques, huge amounts of time series data are generated and accumulated in various domains, fueling plenty of real-world applications. In this setting, time series anomaly detection is practically important. It endeavors to identify deviant samples from the normal sample distribution in time series. Existing approaches generally assume that all the time series is available at a central location. However, we are witnessing the decentralized collection of time series due to the deployment of various edge devices. To bridge the gap between the decentralized time series data and the centralized anomaly detection algorithms, we propose a Parameter-efficient Federated Anomaly Detection framework named PeFAD with the increasing privacy concerns. PeFAD for the first time employs the pre-trained language model (PLM) as the body of the client's local model, which can benefit from its cross-modality knowledge transfer capability. To reduce the communication overhead and local model adaptation cost, we propose a parameter-efficient federated training module such that clients only need to fine-tune small-scale parameters and transmit them to the server for update. PeFAD utilizes a novel anomaly-driven mask selection strategy to mitigate the impact of neglected anomalies during training. A knowledge distillation operation on a synthetic privacy-preserving dataset that is shared by all the clients is also proposed to address the data heterogeneity issue across clients. We conduct extensive evaluations on four real datasets, where PeFAD outperforms existing state-of-the-art baselines by up to 28.74%.
|
http://arxiv.org/pdf/2406.02318v2
|
[
"Ronghui Xu",
"Hao Miao",
"Senzhang Wang",
"Philip S. Yu",
"Jianxin Wang"
] |
2024-07-04T11:00:25Z
|
2024-06-04T13:51:08Z
|
2407.03250
|
When big data actually are low-rank, or entrywise approximation of
certain function-generated matrices
|
The article concerns low-rank approximation of matrices generated by sampling a smooth function of two $m$-dimensional variables. We refute an argument made in the literature that, for a specific class of analytic functions, such matrices admit accurate entrywise approximation of rank that is independent of $m$. We provide a theoretical explanation of the numerical results presented in support of this argument, describing three narrower classes of functions for which $n times n$ function-generated matrices can be approximated within an entrywise error of order $varepsilon$ with rank $mathcal{O}(log(n) varepsilon^{-2} mathrm{polylog}(varepsilon^{-1}))$ that is independent of the dimension $m$: (i) functions of the inner product of the two variables, (ii) functions of the squared Euclidean distance between the variables, and (iii) shift-invariant positive-definite kernels. We extend our argument to low-rank tensor-train approximation of tensors generated with functions of the multi-linear product of their $m$-dimensional variables. We discuss our results in the context of low-rank approximation of attention in transformer neural networks.
|
http://arxiv.org/pdf/2407.03250v2
|
[
"Stanislav Budzinskiy"
] |
2024-07-04T10:56:45Z
|
2024-07-03T16:29:47Z
|
2407.03824
|
Emergent Interpretable Symbols and Content-Style Disentanglement via
Variance-Invariance Constraints
|
We contribute an unsupervised method that effectively learns from raw observation and disentangles its latent space into content and style representations. Unlike most disentanglement algorithms that rely on domain-specific labels and knowledge, our method is based on the insight of domain-general statistical differences between content and style -- content varies more among different fragments within a sample but maintains an invariant vocabulary across data samples, whereas style remains relatively invariant within a sample but exhibits more significant variation across different samples. We integrate such inductive bias into an encoder-decoder architecture and name our method after V3 (variance-versus-invariance). Experimental results show that V3 generalizes across two distinct domains in different modalities, music audio and images of written digits, successfully learning pitch-timbre and digit-color disentanglements, respectively. Also, the disentanglement robustness significantly outperforms baseline unsupervised methods and is even comparable to supervised counterparts. Furthermore, symbolic-level interpretability emerges in the learned codebook of content, forging a near one-to-one alignment between machine representation and human knowledge.
|
http://arxiv.org/pdf/2407.03824v1
|
[
"Yuxuan Wu",
"Ziyu Wang",
"Bhiksha Raj",
"Gus Xia"
] |
2024-07-04T10:52:02Z
|
2024-07-04T10:52:02Z
|
2407.03821
|
Seamless Monitoring of Stress Levels Leveraging a Universal Model for
Time Sequences
|
Monitoring the stress level in patients with neurodegenerative diseases can help manage symptoms, improve patient's quality of life, and provide insight into disease progression. In the literature, ECG, actigraphy, speech, voice, and facial analysis have proven effective at detecting patients' emotions. On the other hand, these tools are invasive and do not integrate smoothly into the patient's daily life. HRV has also been proven to effectively indicate stress conditions, especially in combination with other signals. However, when HRV is derived from less invasive devices than the ECG, like smartwatches and bracelets, the quality of measurements significantly degrades. This paper presents a methodology for stress detection from a smartwatch based on a universal model for time series, UniTS, which we fine-tuned for the task. We cast the problem as anomaly detection rather than classification to favor model adaptation to individual patients and allow the clinician to maintain greater control over the system's predictions. We demonstrate that our proposed model considerably surpasses 12 top-performing methods on 3 benchmark datasets. Furthermore, unlike other state-of-the-art systems, UniTS enables seamless monitoring, as it shows comparable performance when using signals from invasive or lightweight devices.
|
http://arxiv.org/pdf/2407.03821v1
|
[
"Davide Gabrielli",
"Bardh Prenkaj",
"Paola Velardi"
] |
2024-07-04T10:46:09Z
|
2024-07-04T10:46:09Z
|
2312.11269
|
Spherical Mask: Coarse-to-Fine 3D Point Cloud Instance Segmentation with
Spherical Representation
|
Coarse-to-fine 3D instance segmentation methods show weak performances compared to recent Grouping-based, Kernel-based and Transformer-based methods. We argue that this is due to two limitations: 1) Instance size overestimation by axis-aligned bounding box(AABB) 2) False negative error accumulation from inaccurate box to the refinement phase. In this work, we introduce Spherical Mask, a novel coarse-to-fine approach based on spherical representation, overcoming those two limitations with several benefits. Specifically, our coarse detection estimates each instance with a 3D polygon using a center and radial distance predictions, which avoids excessive size estimation of AABB. To cut the error propagation in the existing coarse-to-fine approaches, we virtually migrate points based on the polygon, allowing all foreground points, including false negatives, to be refined. During inference, the proposal and point migration modules run in parallel and are assembled to form binary masks of instances. We also introduce two margin-based losses for the point migration to enforce corrections for the false positives/negatives and cohesion of foreground points, significantly improving the performance. Experimental results from three datasets, such as ScanNetV2, S3DIS, and STPLS3D, show that our proposed method outperforms existing works, demonstrating the effectiveness of the new instance representation with spherical coordinates. The code is available at: https://github.com/yunshin/SphericalMask
|
http://arxiv.org/pdf/2312.11269v2
|
[
"Sangyun Shin",
"Kaichen Zhou",
"Madhu Vankadari",
"Andrew Markham",
"Niki Trigoni"
] |
2024-07-04T10:29:32Z
|
2023-12-18T15:14:07Z
|
2407.03804
|
Multi-Time Scale Service Caching and Pricing in MEC Systems with Dynamic
Program Popularity
|
In mobile edge computing systems, base stations (BSs) equipped with edge servers can provide computing services to users to reduce their task execution time. However, there is always a conflict of interest between the BS and users. The BS prices the service programs based on user demand to maximize its own profit, while the users determine their offloading strategies based on the prices to minimize their costs. Moreover, service programs need to be pre-cached to meet immediate computing needs. Due to the limited caching capacity and variations in service program popularity, the BS must dynamically select which service programs to cache. Since service caching and pricing have different needs for adjustment time granularities, we propose a two-time scale framework to jointly optimize service caching, pricing and task offloading. For the large time scale, we propose a game-nested deep reinforcement learning algorithm to dynamically adjust service caching according to the estimated popularity information. For the small time scale, by modeling the interaction between the BS and users as a two-stage game, we prove the existence of the equilibrium under incomplete information and then derive the optimal pricing and offloading strategies. Extensive simulations based on a real-world dataset demonstrate the efficiency of the proposed approach.
|
http://arxiv.org/pdf/2407.03804v1
|
[
"Yiming Chen",
"Xingyuan Hu",
"Bo Gu",
"Shimin Gong",
"Zhou Su"
] |
2024-07-04T10:23:56Z
|
2024-07-04T10:23:56Z
|
2306.17815
|
Bayesian Optimization with Formal Safety Guarantees via Online Conformal
Prediction
|
Black-box zero-th order optimization is a central primitive for applications in fields as diverse as finance, physics, and engineering. In a common formulation of this problem, a designer sequentially attempts candidate solutions, receiving noisy feedback on the value of each attempt from the system. In this paper, we study scenarios in which feedback is also provided on the safety of the attempted solution, and the optimizer is constrained to limit the number of unsafe solutions that are tried throughout the optimization process. Focusing on methods based on Bayesian optimization (BO), prior art has introduced an optimization scheme -- referred to as SAFEOPT -- that is guaranteed not to select any unsafe solution with a controllable probability over feedback noise as long as strict assumptions on the safety constraint function are met. In this paper, a novel BO-based approach is introduced that satisfies safety requirements irrespective of properties of the constraint function. This strong theoretical guarantee is obtained at the cost of allowing for an arbitrary, controllable but non-zero, rate of violation of the safety constraint. The proposed method, referred to as SAFE-BOCP, builds on online conformal prediction (CP) and is specialized to the cases in which feedback on the safety constraint is either noiseless or noisy. Experimental results on synthetic and real-world data validate the advantages and flexibility of the proposed SAFE-BOCP.
|
http://arxiv.org/pdf/2306.17815v3
|
[
"Yunchuan Zhang",
"Sangwoo Park",
"Osvaldo Simeone"
] |
2024-07-04T10:23:05Z
|
2023-06-30T17:26:49Z
|
2402.17398
|
A Quantum Approach to Synthetic Minority Oversampling Technique (SMOTE)
|
The paper proposes the Quantum-SMOTE method, a novel solution that uses quantum computing techniques to solve the prevalent problem of class imbalance in machine learning datasets. Quantum-SMOTE, inspired by the Synthetic Minority Oversampling Technique (SMOTE), generates synthetic data points using quantum processes such as swap tests and quantum rotation. The process varies from the conventional SMOTE algorithm's usage of K-Nearest Neighbors (KNN) and Euclidean distances, enabling synthetic instances to be generated from minority class data points without relying on neighbor proximity. The algorithm asserts greater control over the synthetic data generation process by introducing hyperparameters such as rotation angle, minority percentage, and splitting factor, which allow for customization to specific dataset requirements. Due to the use of a compact swap test, the algorithm can accommodate a large number of features. Furthermore, the approach is tested on a public dataset of Telecom Churn and evaluated alongside two prominent classification algorithms, Random Forest and Logistic Regression, to determine its impact along with varying proportions of synthetic data.
|
http://arxiv.org/pdf/2402.17398v3
|
[
"Nishikanta Mohanty",
"Bikash K. Behera",
"Christopher Ferrie",
"Pravat Dash"
] |
2024-07-04T10:06:23Z
|
2024-02-27T10:46:36Z
|
2407.03792
|
NeuroSteiner: A Graph Transformer for Wirelength Estimation
|
A core objective of physical design is to minimize wirelength (WL) when placing chip components on a canvas. Computing the minimal WL of a placement requires finding rectilinear Steiner minimum trees (RSMTs), an NP-hard problem. We propose NeuroSteiner, a neural model that distills GeoSteiner, an optimal RSMT solver, to navigate the cost--accuracy frontier of WL estimation. NeuroSteiner is trained on synthesized nets labeled by GeoSteiner, alleviating the need to train on real chip designs. Moreover, NeuroSteiner's differentiability allows to place by minimizing WL through gradient descent. On ISPD 2005 and 2019, NeuroSteiner can obtain 0.3% WL error while being 60% faster than GeoSteiner, or 0.2% and 30%.
|
http://arxiv.org/pdf/2407.03792v1
|
[
"Sahil Manchanda",
"Dana Kianfar",
"Markus Peschl",
"Romain Lepert",
"Michaël Defferrard"
] |
2024-07-04T09:55:22Z
|
2024-07-04T09:55:22Z
|
2407.06083
|
A Survey of Controllable Learning: Methods and Applications in
Information Retrieval
|
Controllable learning (CL) emerges as a critical component in trustworthy machine learning, ensuring that learners meet predefined targets and can adaptively adjust without retraining according to the changes in those targets. We provide a formal definition of CL, and discuss its applications in information retrieval (IR) where information needs are often complex and dynamic. The survey categorizes CL according to who controls (users or platforms), what is controllable (e.g., retrieval objectives, users' historical behaviors, controllable environmental adaptation), how control is implemented (e.g., rule-based method, Pareto optimization, Hypernetwork), and where to implement control (e.g.,pre-processing, in-processing, post-processing methods). Then, we identify challenges faced by CL across training, evaluation, task setting, and deployment in online environments. Additionally, we outline promising directions for CL in theoretical analysis, efficient computation, empowering large language models, application scenarios and evaluation frameworks in IR.
|
http://arxiv.org/pdf/2407.06083v1
|
[
"Chenglei Shen",
"Xiao Zhang",
"Teng Shi",
"Changshuo Zhang",
"Guofu Xie",
"Jun Xu"
] |
2024-07-04T09:50:50Z
|
2024-07-04T09:50:50Z
|
2407.03779
|
Functional Faithfulness in the Wild: Circuit Discovery with
Differentiable Computation Graph Pruning
|
In this paper, we introduce a comprehensive reformulation of the task known as Circuit Discovery, along with DiscoGP, a novel and effective algorithm based on differentiable masking for discovering circuits. Circuit discovery is the task of interpreting the computational mechanisms of language models (LMs) by dissecting their functions and capabilities into sparse subnetworks (circuits). We identified two major limitations in existing circuit discovery efforts: (1) a dichotomy between weight-based and connection-edge-based approaches forces researchers to choose between pruning connections or weights, thereby limiting the scope of mechanistic interpretation of LMs; (2) algorithms based on activation patching tend to identify circuits that are neither functionally faithful nor complete. The performance of these identified circuits is substantially reduced, often resulting in near-random performance in isolation. Furthermore, the complement of the circuit -- i.e., the original LM with the identified circuit removed -- still retains adequate performance, indicating that essential components of a complete circuits are missed by existing methods. DiscoGP successfully addresses the two aforementioned issues and demonstrates state-of-the-art faithfulness, completeness, and sparsity. The effectiveness of the algorithm and its novel structure open up new avenues of gathering new insights into the internal workings of generative AI.
|
http://arxiv.org/pdf/2407.03779v1
|
[
"Lei Yu",
"Jingcheng Niu",
"Zining Zhu",
"Gerald Penn"
] |
2024-07-04T09:42:25Z
|
2024-07-04T09:42:25Z
|
2405.18236
|
Position Paper: Think Globally, React Locally -- Bringing Real-time
Reference-based Website Phishing Detection on macOS
|
Background. The recent surge in phishing attacks keeps undermining the effectiveness of the traditional anti-phishing blacklist approaches. On-device anti-phishing solutions are gaining popularity as they offer faster phishing detection locally. Aim. We aim to eliminate the delay in recognizing and recording phishing campaigns in databases via on-device solutions that identify phishing sites immediately when encountered by the user rather than waiting for a web crawler's scan to finish. Additionally, utilizing operating system-specific resources and frameworks, we aim to minimize the impact on system performance and depend on local processing to protect user privacy. Method. We propose a phishing detection solution that uses a combination of computer vision and on-device machine learning models to analyze websites in real time. Our reference-based approach analyzes the visual content of webpages, identifying phishing attempts through layout analysis, credential input areas detection, and brand impersonation criteria combination. Results. Our case study shows it's feasible to perform background processing on-device continuously, for the case of the web browser requiring the resource use of 16% of a single CPU core and less than 84MB of RAM on Apple M1 while maintaining the accuracy of brand logo detection at 46.6% (comparable with baselines), and of Credential Requiring Page detection at 98.1% (improving the baseline by 3.1%), within the test dataset. Conclusions. Our results demonstrate the potential of on-device, real-time phishing detection systems to enhance cybersecurity defensive technologies and extend the scope of phishing detection to more similar regions of interest, e.g., email clients and messenger windows.
|
http://arxiv.org/pdf/2405.18236v2
|
[
"Ivan Petrukha",
"Nataliia Stulova",
"Sergii Kryvoblotskyi"
] |
2024-07-04T09:37:24Z
|
2024-05-28T14:46:03Z
|
2312.01072
|
A Survey of Temporal Credit Assignment in Deep Reinforcement Learning
|
The Credit Assignment Problem (CAP) refers to the longstanding challenge of Reinforcement Learning (RL) agents to associate actions with their long-term consequences. Solving the CAP is a crucial step towards the successful deployment of RL in the real world since most decision problems provide feedback that is noisy, delayed, and with little or no information about the causes. These conditions make it hard to distinguish serendipitous outcomes from those caused by informed decision-making. However, the mathematical nature of credit and the CAP remains poorly understood and defined. In this survey, we review the state of the art of Temporal Credit Assignment (CA) in deep RL. We propose a unifying formalism for credit that enables equitable comparisons of state-of-the-art algorithms and improves our understanding of the trade-offs between the various methods. We cast the CAP as the problem of learning the influence of an action over an outcome from a finite amount of experience. We discuss the challenges posed by delayed effects, transpositions, and a lack of action influence, and analyse how existing methods aim to address them. Finally, we survey the protocols to evaluate a credit assignment method and suggest ways to diagnose the sources of struggle for different methods. Overall, this survey provides an overview of the field for new-entry practitioners and researchers, it offers a coherent perspective for scholars looking to expedite the starting stages of a new study on the CAP, and it suggests potential directions for future research.
|
http://arxiv.org/pdf/2312.01072v2
|
[
"Eduardo Pignatelli",
"Johan Ferret",
"Matthieu Geist",
"Thomas Mesnard",
"Hado van Hasselt",
"Olivier Pietquin",
"Laura Toni"
] |
2024-07-04T09:32:18Z
|
2023-12-02T08:49:51Z
|
2407.03760
|
GraphCNNpred: A stock market indices prediction using a Graph based deep
learning system
|
Deep learning techniques for predicting stock market prices is an popular topic in the field of data science. Customized feature engineering arises as pre-processing tools of different stock market dataset. In this paper, we give a graph neural network based convolutional neural network (CNN) model, that can be applied on diverse source of data, in the attempt to extract features to predict the trends of indices of text{S}&text{P} 500, NASDAQ, DJI, NYSE, and RUSSEL.
|
http://arxiv.org/pdf/2407.03760v1
|
[
"Yuhui Jin"
] |
2024-07-04T09:14:24Z
|
2024-07-04T09:14:24Z
|
2407.03759
|
Convolutional vs Large Language Models for Software Log Classification
in Edge-Deployable Cellular Network Testing
|
Software logs generated by sophisticated network emulators in the telecommunications industry, such as VIAVI TM500, are extremely complex, often comprising tens of thousands of text lines with minimal resemblance to natural language. Only specialised expert engineers can decipher such logs and troubleshoot defects in test runs. While AI offers a promising solution for automating defect triage, potentially leading to massive revenue savings for companies, state-of-the-art large language models (LLMs) suffer from significant drawbacks in this specialised domain. These include a constrained context window, limited applicability to text beyond natural language, and high inference costs. To address these limitations, we propose a compact convolutional neural network (CNN) architecture that offers a context window spanning up to 200,000 characters and achieves over 96% accuracy (F1>0.9) in classifying multifaceted software logs into various layers in the telecommunications protocol stack. Specifically, the proposed model is capable of identifying defects in test runs and triaging them to the relevant department, formerly a manual engineering process that required expert knowledge. We evaluate several LLMs; LLaMA2-7B, Mixtral 8x7B, Flan-T5, BERT and BigBird, and experimentally demonstrate their shortcomings in our specialized application. Despite being lightweight, our CNN significantly outperforms LLM-based approaches in telecommunications log classification while minimizing the cost of production. Our defect triaging AI model is deployable on edge devices without dedicated hardware and widely applicable across software logs in various industries.
|
http://arxiv.org/pdf/2407.03759v1
|
[
"Achintha Ihalage",
"Sayed M. Taheri",
"Faris Muhammad",
"Hamed Al-Raweshidy"
] |
2024-07-04T09:12:08Z
|
2024-07-04T09:12:08Z
|
2402.05806
|
On Temperature Scaling and Conformal Prediction of Deep Classifiers
|
In many classification applications, the prediction of a deep neural network (DNN) based classifier needs to be accompanied by some confidence indication. Two popular approaches for that aim are: 1) Calibration: modifies the classifier's softmax values such that the maximal value better estimates the correctness probability; and 2) Conformal Prediction (CP): produces a prediction set of candidate labels that contains the true label with a user-specified probability, guaranteeing marginal coverage, rather than, e.g., per class coverage. In practice, both types of indications are desirable, yet, so far the interplay between them has not been investigated. We start this paper with an extensive empirical study of the effect of the popular Temperature Scaling (TS) calibration on prominent CP methods and reveal that while it improves the class-conditional coverage of adaptive CP methods, surprisingly, it negatively affects their prediction set sizes. Subsequently, we explore the effect of TS beyond its calibration application and offer simple guidelines for practitioners to trade prediction set size and conditional coverage of adaptive CP methods while effectively combining them with calibration. Finally, we present a theoretical analysis of the effect of TS on the prediction set sizes, revealing several mathematical properties of the procedure, according to which we provide reasoning for this unintuitive phenomenon.
|
http://arxiv.org/pdf/2402.05806v2
|
[
"Lahav Dabah",
"Tom Tirer"
] |
2024-07-04T08:59:21Z
|
2024-02-08T16:45:12Z
|
2402.03201
|
Guidance with Spherical Gaussian Constraint for Conditional Diffusion
|
Recent advances in diffusion models attempt to handle conditional generative tasks by utilizing a differentiable loss function for guidance without the need for additional training. While these methods achieved certain success, they often compromise on sample quality and require small guidance step sizes, leading to longer sampling processes. This paper reveals that the fundamental issue lies in the manifold deviation during the sampling process when loss guidance is employed. We theoretically show the existence of manifold deviation by establishing a certain lower bound for the estimation error of the loss guidance. To mitigate this problem, we propose Diffusion with Spherical Gaussian constraint (DSG), drawing inspiration from the concentration phenomenon in high-dimensional Gaussian distributions. DSG effectively constrains the guidance step within the intermediate data manifold through optimization and enables the use of larger guidance steps. Furthermore, we present a closed-form solution for DSG denoising with the Spherical Gaussian constraint. Notably, DSG can seamlessly integrate as a plugin module within existing training-free conditional diffusion methods. Implementing DSG merely involves a few lines of additional code with almost no extra computational overhead, yet it leads to significant performance improvements. Comprehensive experimental results in various conditional generation tasks validate the superiority and adaptability of DSG in terms of both sample quality and time efficiency.
|
http://arxiv.org/pdf/2402.03201v4
|
[
"Lingxiao Yang",
"Shutong Ding",
"Yifan Cai",
"Jingyi Yu",
"Jingya Wang",
"Ye Shi"
] |
2024-07-04T08:58:36Z
|
2024-02-05T17:12:21Z
|
2405.03924
|
NeurDB: An AI-powered Autonomous Data System
|
In the wake of rapid advancements in artificial intelligence (AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB (AIxDB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, self-driving capabilities for improved system performance, etc. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan.
|
http://arxiv.org/pdf/2405.03924v2
|
[
"Beng Chin Ooi",
"Shaofeng Cai",
"Gang Chen",
"Yanyan Shen",
"Kian-Lee Tan",
"Yuncheng Wu",
"Xiaokui Xiao",
"Naili Xing",
"Cong Yue",
"Lingze Zeng",
"Meihui Zhang",
"Zhanhao Zhao"
] |
2024-07-04T08:48:45Z
|
2024-05-07T00:51:48Z
|
2407.03738
|
BasisN: Reprogramming-Free RRAM-Based In-Memory-Computing by Basis
Combination for Deep Neural Networks
|
Deep neural networks (DNNs) have made breakthroughs in various fields including image recognition and language processing. DNNs execute hundreds of millions of multiply-and-accumulate (MAC) operations. To efficiently accelerate such computations, analog in-memory-computing platforms have emerged leveraging emerging devices such as resistive RAM (RRAM). However, such accelerators face the hurdle of being required to have sufficient on-chip crossbars to hold all the weights of a DNN. Otherwise, RRAM cells in the crossbars need to be reprogramed to process further layers, which causes huge time/energy overhead due to the extremely slow writing and verification of the RRAM cells. As a result, it is still not possible to deploy such accelerators to process large-scale DNNs in industry. To address this problem, we propose the BasisN framework to accelerate DNNs on any number of available crossbars without reprogramming. BasisN introduces a novel representation of the kernels in DNN layers as combinations of global basis vectors shared between all layers with quantized coefficients. These basis vectors are written to crossbars only once and used for the computations of all layers with marginal hardware modification. BasisN also provides a novel training approach to enhance computation parallelization with the global basis vectors and optimize the coefficients to construct the kernels. Experimental results demonstrate that cycles per inference and energy-delay product were reduced to below 1% compared with applying reprogramming on crossbars in processing large-scale DNNs such as DenseNet and ResNet on ImageNet and CIFAR100 datasets, while the training and hardware costs are negligible.
|
http://arxiv.org/pdf/2407.03738v1
|
[
"Amro Eldebiky",
"Grace Li Zhang",
"Xunzhao Yin",
"Cheng Zhuo",
"Ing-Chao Lin",
"Ulf Schlichtmann",
"Bing Li"
] |
2024-07-04T08:47:05Z
|
2024-07-04T08:47:05Z
|
2407.03736
|
Semantic Grouping Network for Audio Source Separation
|
Recently, audio-visual separation approaches have taken advantage of the natural synchronization between the two modalities to boost audio source separation performance. They extracted high-level semantics from visual inputs as the guidance to help disentangle sound representation for individual sources. Can we directly learn to disentangle the individual semantics from the sound itself? The dilemma is that multiple sound sources are mixed together in the original space. To tackle the difficulty, in this paper, we present a novel Semantic Grouping Network, termed as SGN, that can directly disentangle sound representations and extract high-level semantic information for each source from input audio mixture. Specifically, SGN aggregates category-wise source features through learnable class tokens of sounds. Then, the aggregated semantic features can be used as the guidance to separate the corresponding audio sources from the mixture. We conducted extensive experiments on music-only and universal sound separation benchmarks: MUSIC, FUSS, MUSDB18, and VGG-Sound. The results demonstrate that our SGN significantly outperforms previous audio-only methods and audio-visual models without utilizing additional visual cues.
|
http://arxiv.org/pdf/2407.03736v1
|
[
"Shentong Mo",
"Yapeng Tian"
] |
2024-07-04T08:37:47Z
|
2024-07-04T08:37:47Z
|
2407.03734
|
Improving Self-supervised Pre-training using Accent-Specific Codebooks
|
Speech accents present a serious challenge to the performance of state-of-the-art end-to-end Automatic Speech Recognition (ASR) systems. Even with self-supervised learning and pre-training of ASR models, accent invariance is seldom achieved. In this work, we propose an accent-aware adaptation technique for self-supervised learning that introduces a trainable set of accent-specific codebooks to the self-supervised architecture. These learnable codebooks enable the model to capture accent specific information during pre-training, that is further refined during ASR finetuning. On the Mozilla Common Voice dataset, our proposed approach outperforms all other accent-adaptation approaches on both seen and unseen English accents, with up to 9% relative reduction in word error rate (WER).
|
http://arxiv.org/pdf/2407.03734v1
|
[
"Darshan Prabhu",
"Abhishek Gupta",
"Omkar Nitsure",
"Preethi Jyothi",
"Sriram Ganapathy"
] |
2024-07-04T08:33:52Z
|
2024-07-04T08:33:52Z
|
2311.17750
|
Addressing Membership Inference Attack in Federated Learning with Model
Compression
|
Federated Learning (FL) has been proposed as a privacy-preserving solution for machine learning. However, recent works have reported that FL can leak private client data through membership inference attacks. In this paper, we show that the effectiveness of these attacks on the clients negatively correlates with the size of the client's datasets and model complexity. Based on this finding, we study the capabilities of model-agnostic Federated Learning to preserve privacy, as it enables the use of models of varying complexity in the clients. To systematically study this topic, we first propose a taxonomy of model-agnostic FL methods according to the strategies adopted by the clients to select the sub-models from the server's model. This taxonomy provides a framework for existing model-agnostic FL approaches and leads to the proposal of new FL methods to fill the gaps in the taxonomy. Next, we analyze the privacy-performance trade-off of all the model-agnostic FL architectures as per the proposed taxonomy when subjected to 3 different membership inference attacks on the CIFAR-10 and CIFAR-100 vision datasets. In our experiments, we find that randomness in the strategy used to select the server's sub-model to train the clients' models can control the clients' privacy while keeping competitive performance on the server's side.
|
http://arxiv.org/pdf/2311.17750v2
|
[
"Gergely Dániel Németh",
"Miguel Ángel Lozano",
"Novi Quadrianto",
"Nuria Oliver"
] |
2024-07-04T08:33:33Z
|
2023-11-29T15:54:15Z
|
2407.03728
|
Measuring Orthogonality in Representations of Generative Models
|
In unsupervised representation learning, models aim to distill essential features from high-dimensional data into lower-dimensional learned representations, guided by inductive biases. Understanding the characteristics that make a good representation remains a topic of ongoing research. Disentanglement of independent generative processes has long been credited with producing high-quality representations. However, focusing solely on representations that adhere to the stringent requirements of most disentanglement metrics, may result in overlooking many high-quality representations, well suited for various downstream tasks. These metrics often demand that generative factors be encoded in distinct, single dimensions aligned with the canonical basis of the representation space. Motivated by these observations, we propose two novel metrics: Importance-Weighted Orthogonality (IWO) and Importance-Weighted Rank (IWR). These metrics evaluate the mutual orthogonality and rank of generative factor subspaces. Throughout extensive experiments on common downstream tasks, over several benchmark datasets and models, IWO and IWR consistently show stronger correlations with downstream task performance than traditional disentanglement metrics. Our findings suggest that representation quality is closer related to the orthogonality of independent generative processes rather than their disentanglement, offering a new direction for evaluating and improving unsupervised learning models.
|
http://arxiv.org/pdf/2407.03728v1
|
[
"Robin C. Geyer",
"Alessandro Torcinovich",
"João B. Carvalho",
"Alexander Meyer",
"Joachim M. Buhmann"
] |
2024-07-04T08:21:54Z
|
2024-07-04T08:21:54Z
|
2407.03718
|
Multi-Convformer: Extending Conformer with Multiple Convolution Kernels
|
Convolutions have become essential in state-of-the-art end-to-end Automatic Speech Recognition~(ASR) systems due to their efficient modelling of local context. Notably, its use in Conformers has led to superior performance compared to vanilla Transformer-based ASR systems. While components other than the convolution module in the Conformer have been reexamined, altering the convolution module itself has been far less explored. Towards this, we introduce Multi-Convformer that uses multiple convolution kernels within the convolution module of the Conformer in conjunction with gating. This helps in improved modeling of local dependencies at varying granularities. Our model rivals existing Conformer variants such as CgMLP and E-Branchformer in performance, while being more parameter efficient. We empirically compare our approach with Conformer and its variants across four different datasets and three different modelling paradigms and show up to 8% relative word error rate~(WER) improvements.
|
http://arxiv.org/pdf/2407.03718v1
|
[
"Darshan Prabhu",
"Yifan Peng",
"Preethi Jyothi",
"Shinji Watanabe"
] |
2024-07-04T08:08:12Z
|
2024-07-04T08:08:12Z
|
2407.03111
|
Compressed Latent Replays for Lightweight Continual Learning on Spiking
Neural Networks
|
Rehearsal-based Continual Learning (CL) has been intensely investigated in Deep Neural Networks (DNNs). However, its application in Spiking Neural Networks (SNNs) has not been explored in depth. In this paper we introduce the first memory-efficient implementation of Latent Replay (LR)-based CL for SNNs, designed to seamlessly integrate with resource-constrained devices. LRs combine new samples with latent representations of previously learned data, to mitigate forgetting. Experiments on the Heidelberg SHD dataset with Sample and Class-Incremental tasks reach a Top-1 accuracy of 92.5% and 92%, respectively, without forgetting the previously learned information. Furthermore, we minimize the LRs' requirements by applying a time-domain compression, reducing by two orders of magnitude their memory requirement, with respect to a naive rehearsal setup, with a maximum accuracy drop of 4%. On a Multi-Class-Incremental task, our SNN learns 10 new classes from an initial set of 10, reaching a Top-1 accuracy of 78.4% on the full test set.
|
http://arxiv.org/pdf/2407.03111v2
|
[
"Alberto Dequino",
"Alessio Carpegna",
"Davide Nadalini",
"Alessandro Savino",
"Luca Benini",
"Stefano Di Carlo",
"Francesco Conti"
] |
2024-07-04T08:07:18Z
|
2024-05-08T09:03:17Z
|
2407.06212
|
Bias Correction in Machine Learning-based Classification of Rare Events
|
Online platform businesses can be identified by using web-scraped texts. This is a classification problem that combines elements of natural language processing and rare event detection. Because online platforms are rare, accurately identifying them with Machine Learning algorithms is challenging. Here, we describe the development of a Machine Learning-based text classification approach that reduces the number of false positives as much as possible. It greatly reduces the bias in the estimates obtained by using calibrated probabilities and ensembles.
|
http://arxiv.org/pdf/2407.06212v1
|
[
"Luuk Gubbels",
"Marco Puts",
"Piet Daas"
] |
2024-07-04T08:02:34Z
|
2024-07-04T08:02:34Z
|
2401.18079
|
KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache
Quantization
|
LLMs are seeing growing use for applications such as document analysis and summarization which require large context windows, and with these large context windows KV cache activations surface as the dominant contributor to memory consumption during inference. Quantization is a promising approach for compressing KV cache activations; however, existing solutions fail to represent activations accurately in ultra-low precisions, such as sub-4-bit. In this work, we present KVQuant, which addresses this problem by incorporating novel methods for quantizing cached KV activations, including: (i) Per-Channel Key Quantization, where we adjust the dimension along which we quantize the Key activations to better match the distribution; (ii) Pre-RoPE Key Quantization, where we quantize Key activations before the rotary positional embedding to mitigate its impact on quantization; (iii) Non-Uniform KV Cache Quantization, where we derive per-layer sensitivity-weighted non-uniform datatypes that better represent the distributions; and (iv) Per-Vector Dense-and-Sparse Quantization, where we isolate outliers separately for each vector to minimize skews in quantization ranges. By applying our method to the LLaMA, Llama-2, Llama-3, and Mistral models, we achieve $<0.1$ perplexity degradation with 3-bit quantization on both Wikitext-2 and C4, outperforming existing approaches. Our method enables serving the LLaMA-7B model with a context length of up to 1 million on a single A100-80GB GPU and up to 10 million on an 8-GPU system.
|
http://arxiv.org/pdf/2401.18079v4
|
[
"Coleman Hooper",
"Sehoon Kim",
"Hiva Mohammadzadeh",
"Michael W. Mahoney",
"Yakun Sophia Shao",
"Kurt Keutzer",
"Amir Gholami"
] |
2024-07-04T08:00:01Z
|
2024-01-31T18:58:14Z
|
2405.02086
|
Multi-level projection with exponential parallel speedup; Application to
sparse auto-encoders neural networks
|
The $ell_{1,infty}$ norm is an efficient structured projection but the complexity of the best algorithm is unfortunately $mathcal{O}big(n m log(n m)big)$ for a matrix in $mathbb{R}^{ntimes m}$. In this paper, we propose a new bi-level projection method for which we show that the time complexity for the $ell_{1,infty}$ norm is only $mathcal{O}big(n m big)$ for a matrix in $mathbb{R}^{ntimes m}$, and $mathcal{O}big(n + m big)$ with full parallel power. We generalize our method to tensors and we propose a new multi-level projection, having an induced decomposition that yields a linear parallel speedup up to an exponential speedup factor, resulting in a time complexity lower-bounded by the sum of the dimensions, instead of the product of the dimensions. we provide a large base of implementation of our framework for bi-level and tri-level (matrices and tensors) for various norms and provides also the parallel implementation. Experiments show that our projection is $2$ times faster than the actual fastest Euclidean algorithms while providing same accuracy and better sparsity in neural networks applications.
|
http://arxiv.org/pdf/2405.02086v2
|
[
"Guillaume Perez",
"Michel Barlaud"
] |
2024-07-04T07:58:17Z
|
2024-05-03T13:21:49Z
|
2407.03704
|
Neural Probabilistic Logic Learning for Knowledge Graph Reasoning
|
Knowledge graph (KG) reasoning is a task that aims to predict unknown facts based on known factual samples. Reasoning methods can be divided into two categories: rule-based methods and KG-embedding based methods. The former possesses precise reasoning capabilities but finds it challenging to reason efficiently over large-scale knowledge graphs. While gaining the ability to reason over large-scale knowledge graphs, the latter sacrifices reasoning accuracy. This paper aims to design a reasoning framework called Neural Probabilistic Logic Learning(NPLL) that achieves accurate reasoning on knowledge graphs. Our approach introduces a scoring module that effectively enhances the expressive power of embedding networks, striking a balance between model simplicity and reasoning capabilities. We improve the interpretability of the model by incorporating a Markov Logic Network based on variational inference. We empirically evaluate our approach on several benchmark datasets, and the experimental results validate that our method substantially enhances the accuracy and quality of the reasoning results.
|
http://arxiv.org/pdf/2407.03704v1
|
[
"Fengsong Sun",
"Jinyu Wang",
"Zhiqing Wei",
"Xianchao Zhang"
] |
2024-07-04T07:45:46Z
|
2024-07-04T07:45:46Z
|
2404.13846
|
Filtered Direct Preference Optimization
|
Reinforcement learning from human feedback (RLHF) plays a crucial role in aligning language models with human preferences. While the significance of dataset quality is generally recognized, explicit investigations into its impact within the RLHF framework, to our knowledge, have been limited. This paper addresses the issue of text quality within the preference dataset by focusing on direct preference optimization (DPO), an increasingly adopted reward-model-free RLHF method. We confirm that text quality significantly influences the performance of models optimized with DPO more than those optimized with reward-model-based RLHF. Building on this new insight, we propose an extension of DPO, termed filtered direct preference optimization (fDPO). fDPO uses a trained reward model to monitor the quality of texts within the preference dataset during DPO training. Samples of lower quality are discarded based on comparisons with texts generated by the model being optimized, resulting in a more accurate dataset. Experimental results demonstrate that fDPO enhances the final model performance. Our code is available at https://github.com/CyberAgentAILab/filtered-dpo.
|
http://arxiv.org/pdf/2404.13846v3
|
[
"Tetsuro Morimura",
"Mitsuki Sakamoto",
"Yuu Jinnai",
"Kenshi Abe",
"Kaito Ariu"
] |
2024-07-04T07:40:53Z
|
2024-04-22T03:05:19Z
|
2407.03700
|
Deep learning architectures for data-driven damage detection in
nonlinear dynamic systems
|
The primary goal of structural health monitoring is to detect damage at its onset before it reaches a critical level. The in-depth investigation in the present work addresses deep learning applied to data-driven damage detection in nonlinear dynamic systems. In particular, autoencoders (AEs) and generative adversarial networks (GANs) are implemented leveraging on 1D convolutional neural networks. The onset of damage is detected in the investigated nonlinear dynamic systems by exciting random vibrations of varying intensity, without prior knowledge of the system or the excitation and in unsupervised manner. The comprehensive numerical study is conducted on dynamic systems exhibiting different types of nonlinear behavior. An experimental application related to a magneto-elastic nonlinear system is also presented to corroborate the conclusions.
|
http://arxiv.org/pdf/2407.03700v1
|
[
"Harrish Joseph",
"Giuseppe Quaranta",
"Biagio Carboni",
"Walter Lacarbonara"
] |
2024-07-04T07:40:02Z
|
2024-07-04T07:40:02Z
|
2407.03689
|
Text2TimeSeries: Enhancing Financial Forecasting through Time Series
Prediction Updates with Event-Driven Insights from Large Language Models
|
Time series models, typically trained on numerical data, are designed to forecast future values. These models often rely on weighted averaging techniques over time intervals. However, real-world time series data is seldom isolated and is frequently influenced by non-numeric factors. For instance, stock price fluctuations are impacted by daily random events in the broader world, with each event exerting a unique influence on price signals. Previously, forecasts in financial markets have been approached in two main ways: either as time-series problems over price sequence or sentiment analysis tasks. The sentiment analysis tasks aim to determine whether news events will have a positive or negative impact on stock prices, often categorizing them into discrete labels. Recognizing the need for a more comprehensive approach to accurately model time series prediction, we propose a collaborative modeling framework that incorporates textual information about relevant events for predictions. Specifically, we leverage the intuition of large language models about future changes to update real number time series predictions. We evaluated the effectiveness of our approach on financial market data.
|
http://arxiv.org/pdf/2407.03689v1
|
[
"Litton Jose Kurisinkel",
"Pruthwik Mishra",
"Yue Zhang"
] |
2024-07-04T07:21:38Z
|
2024-07-04T07:21:38Z
|
2309.16177
|
Sampling Hybrid Climate Simulation at Scale to Reliably Improve Machine
Learning Parameterization
|
Machine-learning (ML) parameterizations of subgrid processes (here of turbulence, convection, and radiation) may one day replace conventional parameterizations by emulating high-resolution physics without the cost of explicit simulation. However, their development has been stymied by uncertainty surrounding whether or not improved offline performance translates to improved online performance (i.e., when coupled to a large-scale general circulation model (GCM)). A key barrier has been the limited sampling of the online effects of the ML design decisions and tuning due to the complexity of performing large ensembles of hybrid physics-ML climate simulations. Our work examines the coupled behavior of full-physics ML parameterizations using large ensembles of hybrid simulations, totalling 2,970 in our case. With extensive sampling, we statistically confirm that lowering offline error lowers online error (given certain constraints). However, we also reveal that decisions decreasing online error, like removing dropout, can trade off against hybrid model stability and vice versa. Nevertheless, we are able to identify design decisions that yield unambiguous improvements to offline and online performance, namely incorporating memory and training on multiple climates. We also find that converting moisture input from specific to relative humidity enhances online stability and that using a Mean Absolute Error (MAE) loss breaks the aforementioned offline/online error relationship. By enabling rapid online experimentation at scale, we empirically answer previously unresolved questions regarding subgrid ML parameterization design.
|
http://arxiv.org/pdf/2309.16177v2
|
[
"Jerry Lin",
"Sungduk Yu",
"Liran Peng",
"Tom Beucler",
"Eliot Wong-Toi",
"Zeyuan Hu",
"Pierre Gentine",
"Margarita Geleta",
"Mike Pritchard"
] |
2024-07-04T07:21:12Z
|
2023-09-28T05:34:29Z
|
2310.12487
|
Improved Operator Learning by Orthogonal Attention
|
Neural operators, as an efficient surrogate model for learning the solutions of PDEs, have received extensive attention in the field of scientific machine learning. Among them, attention-based neural operators have become one of the mainstreams in related research. However, existing approaches overfit the limited training data due to the considerable number of parameters in the attention mechanism. To address this, we develop an orthogonal attention based on the eigendecomposition of the kernel integral operator and the neural approximation of eigenfunctions. The orthogonalization naturally poses a proper regularization effect on the resulting neural operator, which aids in resisting overfitting and boosting generalization. Experiments on six standard neural operator benchmark datasets comprising both regular and irregular geometries show that our method can outperform competing baselines with decent margins.
|
http://arxiv.org/pdf/2310.12487v3
|
[
"Zipeng Xiao",
"Zhongkai Hao",
"Bokai Lin",
"Zhijie Deng",
"Hang Su"
] |
2024-07-04T07:20:40Z
|
2023-10-19T05:47:28Z
|
2402.15472
|
FAIR: Filtering of Automatically Induced Rules
|
The availability of large annotated data can be a critical bottleneck in training machine learning algorithms successfully, especially when applied to diverse domains. Weak supervision offers a promising alternative by accelerating the creation of labeled training data using domain-specific rules. However, it requires users to write a diverse set of high-quality rules to assign labels to the unlabeled data. Automatic Rule Induction (ARI) approaches circumvent this problem by automatically creating rules from features on a small labeled set and filtering a final set of rules from them. In the ARI approach, the crucial step is to filter out a set of a high-quality useful subset of rules from the large set of automatically created rules. In this paper, we propose an algorithm (Filtering of Automatically Induced Rules) to filter rules from a large number of automatically induced rules using submodular objective functions that account for the collective precision, coverage, and conflicts of the rule set. We experiment with three ARI approaches and five text classification datasets to validate the superior performance of our algorithm with respect to several semi-supervised label aggregation approaches. Further, we show that achieves statistically significant results in comparison to existing rule-filtering approaches.
|
http://arxiv.org/pdf/2402.15472v2
|
[
"Divya Jyoti Bajpai",
"Ayush Maheshwari",
"Manjesh Kumar Hanawal",
"Ganesh Ramakrishnan"
] |
2024-07-04T07:06:22Z
|
2024-02-23T18:04:54Z
|
2407.03678
|
Improving Self Consistency in LLMs through Probabilistic Tokenization
|
Prior research has demonstrated noticeable performance gains through the use of probabilistic tokenizations, an approach that involves employing multiple tokenizations of the same input string during the training phase of a language model. Despite these promising findings, modern large language models (LLMs) have yet to be trained using probabilistic tokenizations. Interestingly, while the tokenizers of these contemporary LLMs have the capability to generate multiple tokenizations, this property remains underutilized. In this work, we propose a novel method to leverage the multiple tokenization capabilities of modern LLM tokenizers, aiming to enhance the self-consistency of LLMs in reasoning tasks. Our experiments indicate that when utilizing probabilistic tokenizations, LLMs generate logically diverse reasoning paths, moving beyond mere surface-level linguistic diversity.We carefully study probabilistic tokenization and offer insights to explain the self consistency improvements it brings through extensive experimentation on 5 LLM families and 4 reasoning benchmarks.
|
http://arxiv.org/pdf/2407.03678v1
|
[
"Ashutosh Sathe",
"Divyanshu Aggarwal",
"Sunayana Sitaram"
] |
2024-07-04T06:52:48Z
|
2024-07-04T06:52:48Z
|
2210.08886
|
Learning Decentralized Linear Quadratic Regulators with $\sqrt{T}$
Regret
|
We propose an online learning algorithm that adaptively designs a decentralized linear quadratic regulator when the system model is unknown a priori and new data samples from a single system trajectory become progressively available. The algorithm uses a disturbance-feedback representation of state-feedback controllers coupled with online convex optimization with memory and delayed feedback. Under the assumption that the system is stable or given a known stabilizing controller, we show that our controller enjoys an expected regret that scales as $sqrt{T}$ with the time horizon $T$ for the case of partially nested information pattern. For more general information patterns, the optimal controller is unknown even if the system model is known. In this case, the regret of our controller is shown with respect to a linear sub-optimal controller. We validate our theoretical findings using numerical experiments.
|
http://arxiv.org/pdf/2210.08886v4
|
[
"Lintao Ye",
"Ming Chi",
"Ruiquan Liao",
"Vijay Gupta"
] |
2024-07-04T06:50:53Z
|
2022-10-17T09:29:01Z
|
2306.00985
|
Using generative AI to investigate medical imagery models and datasets
|
AI models have shown promise in many medical imaging tasks. However, our ability to explain what signals these models have learned is severely lacking. Explanations are needed in order to increase the trust in AI-based models, and could enable novel scientific discovery by uncovering signals in the data that are not yet known to experts. In this paper, we present a method for automatic visual explanations leveraging team-based expertise by generating hypotheses of what visual signals in the images are correlated with the task. We propose the following 4 steps: (i) Train a classifier to perform a given task (ii) Train a classifier guided StyleGAN-based image generator (StylEx) (iii) Automatically detect and visualize the top visual attributes that the classifier is sensitive towards (iv) Formulate hypotheses for the underlying mechanisms, to stimulate future research. Specifically, we present the discovered attributes to an interdisciplinary panel of experts so that hypotheses can account for social and structural determinants of health. We demonstrate results on eight prediction tasks across three medical imaging modalities: retinal fundus photographs, external eye photographs, and chest radiographs. We showcase examples of attributes that capture clinically known features, confounders that arise from factors beyond physiological mechanisms, and reveal a number of physiologically plausible novel attributes. Our approach has the potential to enable researchers to better understand, improve their assessment, and extract new knowledge from AI-based models. Importantly, we highlight that attributes generated by our framework can capture phenomena beyond physiology or pathophysiology, reflecting the real world nature of healthcare delivery and socio-cultural factors. Finally, we intend to release code to enable researchers to train their own StylEx models and analyze their predictive tasks.
|
http://arxiv.org/abs/2306.00985v2
|
[
"Oran Lang",
"Doron Yaya-Stupp",
"Ilana Traynis",
"Heather Cole-Lewis",
"Chloe R. Bennett",
"Courtney Lyles",
"Charles Lau",
"Michal Irani",
"Christopher Semturs",
"Dale R. Webster",
"Greg S. Corrado",
"Avinatan Hassidim",
"Yossi Matias",
"Yun Liu",
"Naama Hammel",
"Boris Babenko"
] |
2024-07-04T06:45:05Z
|
2023-06-01T17:59:55Z
|
2407.03672
|
A Survey of Data Synthesis Approaches
|
This paper provides a detailed survey of synthetic data techniques. We first discuss the expected goals of using synthetic data in data augmentation, which can be divided into four parts: 1) Improving Diversity, 2) Data Balancing, 3) Addressing Domain Shift, and 4) Resolving Edge Cases. Synthesizing data are closely related to the prevailing machine learning techniques at the time, therefore, we summarize the domain of synthetic data techniques into four categories: 1) Expert-knowledge, 2) Direct Training, 3) Pre-train then Fine-tune, and 4) Foundation Models without Fine-tuning. Next, we categorize the goals of synthetic data filtering into four types for discussion: 1) Basic Quality, 2) Label Consistency, and 3) Data Distribution. In section 5 of this paper, we also discuss the future directions of synthetic data and state three direction that we believe is important: 1) focus more on quality, 2) the evaluation of synthetic data, and 3) multi-model data augmentation.
|
http://arxiv.org/pdf/2407.03672v1
|
[
"Hsin-Yu Chang",
"Pei-Yu Chen",
"Tun-Hsiang Chou",
"Chang-Sheng Kao",
"Hsuan-Yun Yu",
"Yen-Ting Lin",
"Yun-Nung Chen"
] |
2024-07-04T06:37:09Z
|
2024-07-04T06:37:09Z
|
2308.08268
|
It Ain't That Bad: Understanding the Mysterious Performance Drop in OOD
Generalization for Generative Transformer Models
|
Large language models (LLMs) have achieved remarkable proficiency on solving diverse problems. However, their generalization ability is not always satisfying and the generalization problem is common for generative transformer models in general. Researchers take basic mathematical tasks like n-digit addition or multiplication as important perspectives for investigating their generalization behaviors. It is observed that when training models on n-digit operations (e.g., additions) in which both input operands are n-digit in length, models generalize successfully on unseen n-digit inputs (in-distribution (ID) generalization), but fail miserably on longer, unseen cases (out-of-distribution (OOD) generalization). We bring this unexplained performance drop into attention and ask whether there is systematic OOD generalization. Towards understanding LLMs, we train various smaller language models which may share the same underlying mechanism. We discover that the strong ID generalization stems from structured representations, while behind the unsatisfying OOD performance, the models still exhibit clear learned algebraic structures. Specifically, these models map unseen OOD inputs to outputs with learned equivalence relations in the ID domain, which we call the equivalence generalization. These findings deepen our knowledge regarding the generalizability of generative models including LLMs, and provide insights into potential avenues for improvement.
|
http://arxiv.org/pdf/2308.08268v2
|
[
"Xingcheng Xu",
"Zihao Pan",
"Haipeng Zhang",
"Yanqing Yang"
] |
2024-07-04T06:32:57Z
|
2023-08-16T10:09:42Z
|
2407.03665
|
Heterogeneous Hypergraph Embedding for Recommendation Systems
|
Recent advancements in recommender systems have focused on integrating knowledge graphs (KGs) to leverage their auxiliary information. The core idea of KG-enhanced recommenders is to incorporate rich semantic information for more accurate recommendations. However, two main challenges persist: i) Neglecting complex higher-order interactions in the KG-based user-item network, potentially leading to sub-optimal recommendations, and ii) Dealing with the heterogeneous modalities of input sources, such as user-item bipartite graphs and KGs, which may introduce noise and inaccuracies. To address these issues, we present a novel Knowledge-enhanced Heterogeneous Hypergraph Recommender System (KHGRec). KHGRec captures group-wise characteristics of both the interaction network and the KG, modeling complex connections in the KG. Using a collaborative knowledge heterogeneous hypergraph (CKHG), it employs two hypergraph encoders to model group-wise interdependencies and ensure explainability. Additionally, it fuses signals from the input graphs with cross-view self-supervised learning and attention mechanisms. Extensive experiments on four real-world datasets show our model's superiority over various state-of-the-art baselines, with an average 5.18% relative improvement. Additional tests on noise resilience, missing data, and cold-start problems demonstrate the robustness of our KHGRec framework. Our model and evaluation datasets are publicly available at url{https://github.com/viethungvu1998/KHGRec}.
|
http://arxiv.org/pdf/2407.03665v1
|
[
"Darnbi Sakong",
"Viet Hung Vu",
"Thanh Trung Huynh",
"Phi Le Nguyen",
"Hongzhi Yin",
"Quoc Viet Hung Nguyen",
"Thanh Tam Nguyen"
] |
2024-07-04T06:09:11Z
|
2024-07-04T06:09:11Z
|
2402.17423
|
Reinforced In-Context Black-Box Optimization
|
Black-Box Optimization (BBO) has found successful applications in many fields of science and engineering. Recently, there has been a growing interest in meta-learning particular components of BBO algorithms to speed up optimization and get rid of tedious hand-crafted heuristics. As an extension, learning the entire algorithm from data requires the least labor from experts and can provide the most flexibility. In this paper, we propose RIBBO, a method to reinforce-learn a BBO algorithm from offline data in an end-to-end fashion. RIBBO employs expressive sequence models to learn the optimization histories produced by multiple behavior algorithms and tasks, leveraging the in-context learning ability of large models to extract task information and make decisions accordingly. Central to our method is to augment the optimization histories with textit{regret-to-go} tokens, which are designed to represent the performance of an algorithm based on cumulative regret over the future part of the histories. The integration of regret-to-go tokens enables RIBBO to automatically generate sequences of query points that satisfy the user-desired regret, which is verified by its universally good empirical performance on diverse problems, including BBO benchmark functions, hyper-parameter optimization and robot control problems.
|
http://arxiv.org/pdf/2402.17423v2
|
[
"Lei Song",
"Chenxiao Gao",
"Ke Xue",
"Chenyang Wu",
"Dong Li",
"Jianye Hao",
"Zongzhang Zhang",
"Chao Qian"
] |
2024-07-04T05:41:44Z
|
2024-02-27T11:32:14Z
|
2407.03641
|
Scalable Learned Model Soup on a Single GPU: An Efficient Subspace
Training Strategy
|
Pre-training followed by fine-tuning is widely adopted among practitioners. The performance can be improved by "model soups"~cite{wortsman2022model} via exploring various hyperparameter configurations.The Learned-Soup, a variant of model soups, significantly improves the performance but suffers from substantial memory and time costs due to the requirements of (i) having to load all fine-tuned models simultaneously, and (ii) a large computational graph encompassing all fine-tuned models. In this paper, we propose Memory Efficient Hyperplane Learned Soup (MEHL-Soup) to tackle this issue by formulating the learned soup as a hyperplane optimization problem and introducing block coordinate gradient descent to learn the mixing coefficients. At each iteration, MEHL-Soup only needs to load a few fine-tuned models and build a computational graph with one combined model. We further extend MEHL-Soup to MEHL-Soup+ in a layer-wise manner. Experimental results on various ViT models and data sets show that MEHL-Soup(+) outperforms Learned-Soup(+) in terms of test accuracy, and also reduces memory usage by more than $13times$. Moreover, MEHL-Soup(+) can be run on a single GPU and achieves $9times$ speed up in soup construction compared with the Learned-Soup. The code is released at https://github.com/nblt/MEHL-Soup.
|
http://arxiv.org/pdf/2407.03641v1
|
[
"Tao Li",
"Weisen Jiang",
"Fanghui Liu",
"Xiaolin Huang",
"James T. Kwok"
] |
2024-07-04T05:23:22Z
|
2024-07-04T05:23:22Z
|
2407.03640
|
Generative Technology for Human Emotion Recognition: A Scope Review
|
Affective computing stands at the forefront of artificial intelligence (AI), seeking to imbue machines with the ability to comprehend and respond to human emotions. Central to this field is emotion recognition, which endeavors to identify and interpret human emotional states from different modalities, such as speech, facial images, text, and physiological signals. In recent years, important progress has been made in generative models, including Autoencoder, Generative Adversarial Network, Diffusion Model, and Large Language Model. These models, with their powerful data generation capabilities, emerge as pivotal tools in advancing emotion recognition. However, up to now, there remains a paucity of systematic efforts that review generative technology for emotion recognition. This survey aims to bridge the gaps in the existing literature by conducting a comprehensive analysis of over 320 research papers until June 2024. Specifically, this survey will firstly introduce the mathematical principles of different generative models and the commonly used datasets. Subsequently, through a taxonomy, it will provide an in-depth analysis of how generative techniques address emotion recognition based on different modalities in several aspects, including data augmentation, feature extraction, semi-supervised learning, cross-domain, etc. Finally, the review will outline future research directions, emphasizing the potential of generative models to advance the field of emotion recognition and enhance the emotional intelligence of AI systems.
|
http://arxiv.org/pdf/2407.03640v1
|
[
"Fei Ma",
"Yucheng Yuan",
"Yifan Xie",
"Hongwei Ren",
"Ivan Liu",
"Ying He",
"Fuji Ren",
"Fei Richard Yu",
"Shiguang Ni"
] |
2024-07-04T05:22:55Z
|
2024-07-04T05:22:55Z
|
2311.06217
|
MultiIoT: Benchmarking Machine Learning for the Internet of Things
|
The next generation of machine learning systems must be adept at perceiving and interacting with the physical world through a diverse array of sensory channels. Commonly referred to as the `Internet of Things (IoT)' ecosystem, sensory data from motion, thermal, geolocation, depth, wireless signals, video, and audio are increasingly used to model the states of physical environments and the humans inside them. Despite the potential for understanding human wellbeing, controlling physical devices, and interconnecting smart cities, the community has seen limited benchmarks for building machine learning systems for IoT. Existing efforts are often specialized to a single sensory modality or prediction task, which makes it difficult to study and train large-scale models across many IoT sensors and tasks. To accelerate the development of new machine learning technologies for IoT, this paper proposes MultiIoT, the most expansive and unified IoT benchmark to date, encompassing over 1.15 million samples from 12 modalities and 8 real-world tasks. MultiIoT introduces unique challenges involving (1) generalizable learning from many sensory modalities, (2) multimodal interactions across long temporal ranges, (3) extreme heterogeneity due to unique structure and noise topologies in real-world sensors, and (4) complexity during training and inference. We evaluate a comprehensive set of models on MultiIoT, including modality and task-specific methods, multisensory and multitask supervised models, and large multisensory foundation models. Our results highlight opportunities for ML to make a significant impact in IoT, but many challenges in scalable learning from heterogeneous, long-range, and imperfect sensory modalities still persist. We release all code and data to accelerate future research in machine learning for IoT.
|
http://arxiv.org/pdf/2311.06217v2
|
[
"Shentong Mo",
"Louis-Philippe Morency",
"Russ Salakhutdinov",
"Paul Pu Liang"
] |
2024-07-04T05:16:47Z
|
2023-11-10T18:13:08Z
|
2407.03637
|
HERA: High-efficiency Matrix Compression via Element Replacement
|
Large Language Models (LLMs) have significantly advanced natural language processing tasks such as machine translation, text generation, and sentiment analysis. However, their large size, often consisting of billions of parameters, poses challenges for storage, computation, and deployment, particularly in resource-constrained environments like mobile devices and edge computing platforms. Additionally, the key-value (k-v) cache used to speed up query processing requires substantial memory and storage, exacerbating these challenges. Vector databases have emerged as a crucial technology to efficiently manage and retrieve the high-dimensional vectors produced by LLMs, facilitating faster data access and reducing computational demands. Effective compression and quantization techniques are essential to address these challenges, as they reduce the memory footprint and computational requirements without significantly compromising performance. Traditional methods that uniformly map parameters to compressed spaces often fail to account for the uneven distribution of parameters, leading to considerable accuracy loss. Therefore, innovative approaches are needed to achieve better compression ratios while preserving model performance. In this work, we propose HERA, a novel algorithm that employs heuristic Element Replacement for compressing matrix. HERA systematically replaces elements within the model using heuristic methods, which simplifies the structure of the model and makes subsequent compression more effective. By hierarchically segmenting, compressing, and reorganizing the matrix dataset, our method can effectively reduce the quantization error to 12.3% of the original at the same compression ratio.
|
http://arxiv.org/pdf/2407.03637v1
|
[
"Yanshu Wang",
"Wang Li",
"Tong Yang"
] |
2024-07-04T05:13:58Z
|
2024-07-04T05:13:58Z
|
2407.01392
|
Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion
|
This paper presents Diffusion Forcing, a new training paradigm where a diffusion model is trained to denoise a set of tokens with independent per-token noise levels. We apply Diffusion Forcing to sequence generative modeling by training a causal next-token prediction model to generate one or several future tokens without fully diffusing past ones. Our approach is shown to combine the strengths of next-token prediction models, such as variable-length generation, with the strengths of full-sequence diffusion models, such as the ability to guide sampling to desirable trajectories. Our method offers a range of additional capabilities, such as (1) rolling-out sequences of continuous tokens, such as video, with lengths past the training horizon, where baselines diverge and (2) new sampling and guiding schemes that uniquely profit from Diffusion Forcing's variable-horizon and causal architecture, and which lead to marked performance gains in decision-making and planning tasks. In addition to its empirical success, our method is proven to optimize a variational lower bound on the likelihoods of all subsequences of tokens drawn from the true joint distribution. Project website: https://boyuan.space/diffusion-forcing
|
http://arxiv.org/pdf/2407.01392v3
|
[
"Boyuan Chen",
"Diego Marti Monso",
"Yilun Du",
"Max Simchowitz",
"Russ Tedrake",
"Vincent Sitzmann"
] |
2024-07-04T04:51:10Z
|
2024-07-01T15:43:25Z
|
2407.03631
|
On the performance of sequential Bayesian update for database of diverse
tsunami scenarios
|
Although the sequential tsunami scenario detection framework was validated in our previous work, several tasks remain to be resolved from a practical point of view. This study aims to evaluate the performance of the previous tsunami scenario detection framework using a diverse database consisting of complex fault rupture patterns with heterogeneous slip distributions. Specifically, we compare the effectiveness of scenario superposition to that of the previous most likely scenario detection method. Additionally, how the length of the observation time window influences the accuracy of both methods is analyzed. We utilize an existing database comprising 1771 tsunami scenarios targeting the city Westport (WA, U.S.), which includes synthetic wave height records and inundation distributions as the result of fault rupture in the Cascadia subduction zone. The heterogeneous patterns of slips used in the database increase the diversity of the scenarios and thus make it a proper database for evaluating the performance of scenario superposition. To assess the performance, we consider various observation time windows shorter than 15 minutes and divide the database into five testing and learning sets. The evaluation accuracy of the maximum offshore wave, inundation depth, and its distribution is analyzed to examine the advantages of the scenario superposition method over the previous method. We introduce the dynamic time warping (DTW) method as an additional benchmark and compare its results to that of the Bayesian scenario detection method.
|
http://arxiv.org/pdf/2407.03631v1
|
[
"Reika Nomura",
"Louise A. Hirao Vermare",
"Saneiki Fujita",
"Donsub Rim",
"Shuji Moriguchi",
"Randall J. LeVeque",
"Kenjiro Terada"
] |
2024-07-04T04:46:09Z
|
2024-07-04T04:46:09Z
|
2401.07187
|
A Survey on Statistical Theory of Deep Learning: Approximation, Training
Dynamics, and Generative Models
|
In this article, we review the literature on statistical theories of neural networks from three perspectives. In the first part, results on excess risks for neural networks are reviewed in the nonparametric framework of regression or classification. These results rely on explicit constructions of neural networks, leading to fast convergence rates of excess risks, in that tools from the approximation theory are adopted. Through these constructions, the width and depth of the networks can be expressed in terms of sample size, data dimension, and function smoothness. Nonetheless, their underlying analysis only applies to the global minimizer in the highly non-convex landscape of deep neural networks. This motivates us to review the training dynamics of neural networks in the second part. Specifically, we review papers that attempt to answer ``how the neural network trained via gradient-based methods finds the solution that can generalize well on unseen data.'' In particular, two well-known paradigms are reviewed: the Neural Tangent Kernel (NTK) paradigm, and Mean-Field (MF) paradigm. In the last part, we review the most recent theoretical advancements in generative models including Generative Adversarial Networks (GANs), diffusion models, and in-context learning (ICL) in the Large Language Models (LLMs). The former two models are known to be the main pillars of the modern generative AI era, while ICL is a strong capability of LLMs in learning from a few examples in the context. Finally, we conclude the paper by suggesting several promising directions for deep learning theory.
|
http://arxiv.org/pdf/2401.07187v2
|
[
"Namjoon Suh",
"Guang Cheng"
] |
2024-07-04T04:36:06Z
|
2024-01-14T02:30:19Z
|
2403.07788
|
DexCap: Scalable and Portable Mocap Data Collection System for Dexterous
Manipulation
|
Imitation learning from human hand motion data presents a promising avenue for imbuing robots with human-like dexterity in real-world manipulation tasks. Despite this potential, substantial challenges persist, particularly with the portability of existing hand motion capture (mocap) systems and the complexity of translating mocap data into effective robotic policies. To tackle these issues, we introduce DexCap, a portable hand motion capture system, alongside DexIL, a novel imitation algorithm for training dexterous robot skills directly from human hand mocap data. DexCap offers precise, occlusion-resistant tracking of wrist and finger motions based on SLAM and electromagnetic field together with 3D observations of the environment. Utilizing this rich dataset, DexIL employs inverse kinematics and point cloud-based imitation learning to seamlessly replicate human actions with robot hands. Beyond direct learning from human motion, DexCap also offers an optional human-in-the-loop correction mechanism during policy rollouts to refine and further improve task performance. Through extensive evaluation across six challenging dexterous manipulation tasks, our approach not only demonstrates superior performance but also showcases the system's capability to effectively learn from in-the-wild mocap data, paving the way for future data collection methods in the pursuit of human-level robot dexterity. More details can be found at https://dex-cap.github.io
|
http://arxiv.org/pdf/2403.07788v2
|
[
"Chen Wang",
"Haochen Shi",
"Weizhuo Wang",
"Ruohan Zhang",
"Li Fei-Fei",
"C. Karen Liu"
] |
2024-07-04T04:35:04Z
|
2024-03-12T16:23:49Z
|
2407.03089
|
Spatio-Temporal Adaptive Diffusion Models for EEG Super-Resolution in
Epilepsy Diagnosis
|
Electroencephalogram (EEG) technology, particularly high-density EEG (HD EEG) devices, is widely used in fields such as neuroscience. HD EEG devices improve the spatial resolution of EEG by placing more electrodes on the scalp, meeting the requirements of clinical diagnostic applications such as epilepsy focus localization. However, this technique faces challenges such as high acquisition costs and limited usage scenarios. In this paper, spatio-temporal adaptive diffusion models (STADMs) are proposed to pioneer the use of diffusion models for achieving spatial SR reconstruction from low-resolution (LR, 64 channels or fewer) EEG to high-resolution (HR, 256 channels) EEG. Specifically, a spatio-temporal condition module is designed to extract the spatio-temporal features of LR EEG, which then serve as conditional inputs to guide the reverse denoising process of diffusion models. Additionally, a multi-scale Transformer denoising module is constructed to leverage multi-scale convolution blocks and cross-attention-based diffusion Transformer blocks for conditional guidance to generate subject-adaptive SR EEG. Experimental results demonstrate that the proposed method effectively enhances the spatial resolution of LR EEG and quantitatively outperforms existing methods. Furthermore, STADMs demonstrate their value by applying synthetic SR EEG to classification and source localization tasks of epilepsy patients, indicating their potential to significantly improve the spatial resolution of LR EEG.
|
http://arxiv.org/pdf/2407.03089v2
|
[
"Tong Zhou",
"Shuqiang Wang"
] |
2024-07-04T04:11:57Z
|
2024-07-03T13:26:31Z
|
2407.03622
|
MSfusion: A Dynamic Model Splitting Approach for Resource-Constrained
Machines to Collaboratively Train Larger Models
|
Training large models requires a large amount of data, as well as abundant computation resources. While collaborative learning (e.g., federated learning) provides a promising paradigm to harness collective data from many participants, training large models remains a major challenge for participants with limited resources like mobile devices. We introduce MSfusion, an effective and efficient collaborative learning framework, tailored for training larger models on resourceconstraint machines through model splitting. Specifically, a double shifting model splitting scheme is designed such that in each training round, each participant is assigned a subset of model parameters to train over local data, and aggregates with sub-models of other peers on common parameters. While model splitting significantly reduces the computation and communication costs of individual participants, additional novel designs on adaptive model overlapping and contrastive loss functions help MSfusion to maintain training effectiveness, against model shift across participants. Extensive experiments on image and NLP tasks illustrate significant advantages of MSfusion in performance and efficiency for training large models, and its strong scalability: computation cost of each participant reduces significantly as the number of participants increases.
|
http://arxiv.org/pdf/2407.03622v1
|
[
"Jin Xie",
"Songze Li"
] |
2024-07-04T04:06:24Z
|
2024-07-04T04:06:24Z
|
2407.03601
|
Online Non-Stationary Stochastic Quasar-Convex Optimization
|
Recent research has shown that quasar-convexity can be found in applications such as identification of linear dynamical systems and generalized linear models. Such observations have in turn spurred exciting developments in design and analysis algorithms that exploit quasar-convexity. In this work, we study the online stochastic quasar-convex optimization problems in a dynamic environment. We establish regret bounds of online gradient descent in terms of cumulative path variation and cumulative gradient variance for losses satisfying quasar-convexity and strong quasar-convexity. We then apply the results to generalized linear models (GLM) when the underlying parameter is time-varying. We establish regret bounds of online gradient descent when applying to GLMs with leaky ReLU activation function, logistic activation function, and ReLU activation function. Numerical results are presented to corroborate our findings.
|
http://arxiv.org/pdf/2407.03601v1
|
[
"Yuen-Man Pun",
"Iman Shames"
] |
2024-07-04T03:24:27Z
|
2024-07-04T03:24:27Z
|
2407.03595
|
Machine Learning for Economic Forecasting: An Application to China's GDP
Growth
|
This paper aims to explore the application of machine learning in forecasting Chinese macroeconomic variables. Specifically, it employs various machine learning models to predict the quarterly real GDP growth of China, and analyzes the factors contributing to the performance differences among these models. Our findings indicate that the average forecast errors of machine learning models are generally lower than those of traditional econometric models or expert forecasts, particularly in periods of economic stability. However, during certain inflection points, although machine learning models still outperform traditional econometric models, expert forecasts may exhibit greater accuracy in some instances due to experts' more comprehensive understanding of the macroeconomic environment and real-time economic variables. In addition to macroeconomic forecasting, this paper employs interpretable machine learning methods to identify the key attributive variables from different machine learning models, aiming to enhance the understanding and evaluation of their contributions to macroeconomic fluctuations.
|
http://arxiv.org/pdf/2407.03595v1
|
[
"Yanqing Yang",
"Xingcheng Xu",
"Jinfeng Ge",
"Yan Xu"
] |
2024-07-04T03:04:55Z
|
2024-07-04T03:04:55Z
|
2407.03593
|
Green Multigrid Network
|
GreenLearning networks (GL) directly learn Green's function in physical space, making them an interpretable model for capturing unknown solution operators of partial differential equations (PDEs). For many PDEs, the corresponding Green's function exhibits asymptotic smoothness. In this paper, we propose a framework named Green Multigrid networks (GreenMGNet), an operator learning algorithm designed for a class of asymptotically smooth Green's functions. Compared with the pioneering GL, the new framework presents itself with better accuracy and efficiency, thereby achieving a significant improvement. GreenMGNet is composed of two technical novelties. First, Green's function is modeled as a piecewise function to take into account its singular behavior in some parts of the hyperplane. Such piecewise function is then approximated by a neural network with augmented output(AugNN) so that it can capture singularity accurately. Second, the asymptotic smoothness property of Green's function is used to leverage the Multi-Level Multi-Integration (MLMI) algorithm for both the training and inference stages. Several test cases of operator learning are presented to demonstrate the accuracy and effectiveness of the proposed method. On average, GreenMGNet achieves $3.8%$ to $39.15%$ accuracy improvement. To match the accuracy level of GL, GreenMGNet requires only about $10%$ of the full grid data, resulting in a $55.9%$ and $92.5%$ reduction in training time and GPU memory cost for one-dimensional test problems, and a $37.7%$ and $62.5%$ reduction for two-dimensional test problems.
|
http://arxiv.org/pdf/2407.03593v1
|
[
"Ye Lin",
"Young Ju Lee",
"Jiwei Jia"
] |
2024-07-04T03:02:10Z
|
2024-07-04T03:02:10Z
|
2404.15245
|
Mining Invariance from Nonlinear Multi-Environment Data: Binary
Classification
|
Making predictions in an unseen environment given data from multiple training environments is a challenging task. We approach this problem from an invariance perspective, focusing on binary classification to shed light on general nonlinear data generation mechanisms. We identify a unique form of invariance that exists solely in a binary setting that allows us to train models invariant over environments. We provide sufficient conditions for such invariance and show it is robust even when environmental conditions vary greatly. Our formulation admits a causal interpretation, allowing us to compare it with various frameworks. Finally, we propose a heuristic prediction method and conduct experiments using real and synthetic datasets.
|
http://arxiv.org/pdf/2404.15245v2
|
[
"Austin Goddard",
"Kang Du",
"Yu Xiang"
] |
2024-07-04T02:15:38Z
|
2024-04-23T17:26:59Z
|
2406.19574
|
Deep Temporal Sequence Classification and Mathematical Modeling for Cell
Tracking in Dense 3D Microscopy Videos of Bacterial Biofilms
|
Automatic cell tracking in dense environments is plagued by inaccurate correspondences and misidentification of parent-offspring relationships. In this paper, we introduce a novel cell tracking algorithm named DenseTrack, which integrates deep learning with mathematical model-based strategies to effectively establish correspondences between consecutive frames and detect cell division events in crowded scenarios. We formulate the cell tracking problem as a deep learning-based temporal sequence classification task followed by solving a constrained one-to-one matching optimization problem exploiting the classifier's confidence scores. Additionally, we present an eigendecomposition-based cell division detection strategy that leverages knowledge of cellular geometry. The performance of the proposed approach has been evaluated by tracking densely packed cells in 3D time-lapse image sequences of bacterial biofilm development. The experimental results on simulated as well as experimental fluorescence image sequences suggest that the proposed tracking method achieves superior performance in terms of both qualitative and quantitative evaluation measures compared to recent state-of-the-art cell tracking approaches.
|
http://arxiv.org/pdf/2406.19574v2
|
[
"Tanjin Taher Toma",
"Yibo Wang",
"Andreas Gahlmann",
"Scott T. Acton"
] |
2024-07-04T01:58:27Z
|
2024-06-27T23:26:57Z
|
2407.03574
|
An Axiomatic Definition of Hierarchical Clustering
|
In this paper, we take an axiomatic approach to defining a population hierarchical clustering for piecewise constant densities, and in a similar manner to Lebesgue integration, extend this definition to more general densities. When the density satisfies some mild conditions, e.g., when it has connected support, is continuous, and vanishes only at infinity, or when the connected components of the density satisfy these conditions, our axiomatic definition results in Hartigan's definition of cluster tree.
|
http://arxiv.org/pdf/2407.03574v1
|
[
"Ery Arias-Castro",
"Elizabeth Coda"
] |
2024-07-04T01:53:23Z
|
2024-07-04T01:53:23Z
|
2407.03571
|
A Fully Parameter-Free Second-Order Algorithm for Convex-Concave Minimax
Problems with Optimal Iteration Complexity
|
In this paper, we study second-order algorithms for the convex-concave minimax problem, which has attracted much attention in many fields such as machine learning in recent years. We propose a Lipschitz-free cubic regularization (LF-CR) algorithm for solving the convex-concave minimax optimization problem without knowing the Lipschitz constant. It can be shown that the iteration complexity of the LF-CR algorithm to obtain an $epsilon$-optimal solution with respect to the restricted primal-dual gap is upper bounded by $mathcal{O}(frac{rho|z^0-z^*|^3}{epsilon})^{frac{2}{3}}$, where $z^0=(x^0,y^0)$ is a pair of initial points, $z^*=(x^*,y^*)$ is a pair of optimal solutions, and $rho$ is the Lipschitz constant. We further propose a fully parameter-free cubic regularization (FF-CR) algorithm that does not require any parameters of the problem, including the Lipschitz constant and the upper bound of the distance from the initial point to the optimal solution. We also prove that the iteration complexity of the FF-CR algorithm to obtain an $epsilon$-optimal solution with respect to the gradient norm is upper bounded by $mathcal{O}(frac{rho|z^0-z^*|^2}{epsilon})^{frac{2}{3}}$. Numerical experiments show the efficiency of both algorithms. To the best of our knowledge, the proposed FF-CR algorithm is the first completely parameter-free second-order algorithm for solving convex-concave minimax optimization problems, and its iteration complexity is consistent with the optimal iteration complexity lower bound of existing second-order algorithms with parameters for solving convex-concave minimax problems.
|
http://arxiv.org/pdf/2407.03571v1
|
[
"Junlin Wang",
"Junnan Yang",
"Zi Xu"
] |
2024-07-04T01:46:07Z
|
2024-07-04T01:46:07Z
|
2405.07735
|
Federated Hierarchical Tensor Networks: a Collaborative Learning Quantum
AI-Driven Framework for Healthcare
|
Healthcare industries frequently handle sensitive and proprietary data, and due to strict privacy regulations, they are often reluctant to share data directly. In today's context, Federated Learning (FL) stands out as a crucial remedy, facilitating the rapid advancement of distributed machine learning while effectively managing critical concerns regarding data privacy and governance. The fusion of federated learning and quantum computing represents a groundbreaking interdisciplinary approach with immense potential to revolutionize various industries, from healthcare to finance. In this work, we proposed a federated learning framework based on quantum tensor networks, which leverages the principles of many-body quantum physics. Currently, there are no known classical tensor networks implemented in federated settings. Furthermore, we investigated the effectiveness and feasibility of the proposed framework by conducting a differential privacy analysis to ensure the security of sensitive data across healthcare institutions. Experiments on popular medical image datasets show that the federated quantum tensor network model achieved a mean receiver-operator characteristic area under the curve (ROC-AUC) between 0.91-0.98. Experimental results demonstrate that the quantum federated global model, consisting of highly entangled tensor network structures, showed better generalization and robustness and achieved higher testing accuracy, surpassing the performance of locally trained clients under unbalanced data distributions among healthcare institutions.
|
http://arxiv.org/pdf/2405.07735v2
|
[
"Amandeep Singh Bhatia",
"David E. Bernal Neira"
] |
2024-07-04T01:27:00Z
|
2024-05-13T13:32:02Z
|
2407.03563
|
Learning Video Temporal Dynamics with Cross-Modal Attention for Robust
Audio-Visual Speech Recognition
|
Audio-visual speech recognition (AVSR) aims to transcribe human speech using both audio and video modalities. In practical environments with noise-corrupted audio, the role of video information becomes crucial. However, prior works have primarily focused on enhancing audio features in AVSR, overlooking the importance of video features. In this study, we strengthen the video features by learning three temporal dynamics in video data: context order, playback direction, and the speed of video frames. Cross-modal attention modules are introduced to enrich video features with audio information so that speech variability can be taken into account when training on the video temporal dynamics. Based on our approach, we achieve the state-of-the-art performance on the LRS2 and LRS3 AVSR benchmarks for the noise-dominant settings. Our approach excels in scenarios especially for babble and speech noise, indicating the ability to distinguish the speech signal that should be recognized from lip movements in the video modality. We support the validity of our methodology by offering the ablation experiments for the temporal dynamics losses and the cross-modal attention architecture design.
|
http://arxiv.org/pdf/2407.03563v1
|
[
"Sungnyun Kim",
"Kangwook Jang",
"Sangmin Bae",
"Hoirin Kim",
"Se-Young Yun"
] |
2024-07-04T01:25:20Z
|
2024-07-04T01:25:20Z
|
2407.03557
|
Decision-Focused Evaluation of Worst-Case Distribution Shift
|
Distribution shift is a key challenge for predictive models in practice, creating the need to identify potentially harmful shifts in advance of deployment. Existing work typically defines these worst-case shifts as ones that most degrade the individual-level accuracy of the model. However, when models are used to make a downstream population-level decision like the allocation of a scarce resource, individual-level accuracy may be a poor proxy for performance on the task at hand. We introduce a novel framework that employs a hierarchical model structure to identify worst-case distribution shifts in predictive resource allocation settings by capturing shifts both within and across instances of the decision problem. This task is more difficult than in standard distribution shift settings due to combinatorial interactions, where decisions depend on the joint presence of individuals in the allocation task. We show that the problem can be reformulated as a submodular optimization problem, enabling efficient approximations of worst-case loss. Applying our framework to real data, we find empirical evidence that worst-case shifts identified by one metric often significantly diverge from worst-case distributions identified by other metrics.
|
http://arxiv.org/pdf/2407.03557v1
|
[
"Kevin Ren",
"Yewon Byun",
"Bryan Wilder"
] |
2024-07-04T01:00:53Z
|
2024-07-04T01:00:53Z
|
2308.08487
|
Temporal Interest Network for User Response Prediction
|
User response prediction is essential in industrial recommendation systems, such as online display advertising. Among all the features in recommendation models, user behaviors are among the most critical. Many works have revealed that a user's behavior reflects her interest in the candidate item, owing to the semantic or temporal correlation between behaviors and the candidate. While the literature has individually examined each of these correlations, researchers have yet to analyze them in combination, that is, the semantic-temporal correlation. We empirically measure this correlation and observe intuitive yet robust patterns. We then examine several popular user interest models and find that, surprisingly, none of them learn such correlation well. To fill this gap, we propose a Temporal Interest Network (TIN) to capture the semantic-temporal correlation simultaneously between behaviors and the target. We achieve this by incorporating target-aware temporal encoding, in addition to semantic encoding, to represent behaviors and the target. Furthermore, we conduct explicit 4-way interaction by deploying target-aware attention and target-aware representation to capture both semantic and temporal correlation. We conduct comprehensive evaluations on two popular public datasets, and our proposed TIN outperforms the best-performing baselines by 0.43% and 0.29% on GAUC, respectively. During online A/B testing in Tencent's advertising platform, TIN achieves 1.65% cost lift and 1.93% GMV lift over the base model. It has been successfully deployed in production since October 2023, serving the WeChat Moments traffic. We have released our code at https://github.com/zhouxy1003/TIN.
|
http://arxiv.org/abs/2308.08487v4
|
[
"Haolin Zhou",
"Junwei Pan",
"Xinyi Zhou",
"Xihua Chen",
"Jie Jiang",
"Xiaofeng Gao",
"Guihai Chen"
] |
2024-07-03T23:28:10Z
|
2023-08-15T05:48:44Z
|
2407.03542
|
Probing Perfection: The Relentless Art of Meddling for Pulmonary Airway
Segmentation from HRCT via a Human-AI Collaboration Based Active Learning
Method
|
In pulmonary tracheal segmentation, the scarcity of annotated data is a prevalent issue in medical segmentation. Additionally, Deep Learning (DL) methods face challenges: the opacity of 'black box' models and the need for performance enhancement. Our Human-Computer Interaction (HCI) based models (RS_UNet, LC_UNet, UUNet, and WD_UNet) address these challenges by combining diverse query strategies with various DL models. We train four HCI models and repeat these steps: (1) Query Strategy: The HCI models select samples that provide the most additional representative information when labeled in each iteration and identify unlabeled samples with the greatest predictive disparity using Wasserstein Distance, Least Confidence, Entropy Sampling, and Random Sampling. (2) Central line correction: Selected samples are used for expert correction of system-generated tracheal central lines in each training round. (3) Update training dataset: Experts update the training dataset after each DL model's training epoch, enhancing the trustworthiness and performance of the models. (4) Model training: The HCI model is trained using the updated dataset and an enhanced UNet version. Experimental results confirm the effectiveness of these HCI-based approaches, showing that WD-UNet, LC-UNet, UUNet, and RS-UNet achieve comparable or superior performance to state-of-the-art DL models. Notably, WD-UNet achieves this with only 15%-35% of the training data, reducing physician annotation time by 65%-85%.
|
http://arxiv.org/pdf/2407.03542v1
|
[
"Shiyi Wang",
"Yang Nan",
"Sheng Zhang",
"Federico Felder",
"Xiaodan Xing",
"Yingying Fang",
"Javier Del Ser",
"Simon L F Walsh",
"Guang Yang"
] |
2024-07-03T23:27:53Z
|
2024-07-03T23:27:53Z
|
2307.01050
|
Transport meets Variational Inference: Controlled Monte Carlo Diffusions
|
Connecting optimal transport and variational inference, we present a principled and systematic framework for sampling and generative modelling centred around divergences on path space. Our work culminates in the development of the emph{Controlled Monte Carlo Diffusion} sampler (CMCD) for Bayesian computation, a score-based annealing technique that crucially adapts both forward and backward dynamics in a diffusion model. On the way, we clarify the relationship between the EM-algorithm and iterative proportional fitting (IPF) for Schr{"o}dinger bridges, deriving as well a regularised objective that bypasses the iterative bottleneck of standard IPF-updates. Finally, we show that CMCD has a strong foundation in the Jarzinsky and Crooks identities from statistical physics, and that it convincingly outperforms competing approaches across a wide array of experiments.
|
http://arxiv.org/pdf/2307.01050v9
|
[
"Francisco Vargas",
"Shreyas Padhy",
"Denis Blessing",
"Nikolas Nüsken"
] |
2024-07-03T23:25:33Z
|
2023-07-03T14:28:36Z
|
2308.14938
|
Entropy-based Guidance of Deep Neural Networks for Accelerated
Convergence and Improved Performance
|
Neural networks have dramatically increased our capacity to learn from large, high-dimensional datasets across innumerable disciplines. However, their decisions are not easily interpretable, their computational costs are high, and building and training them are not straightforward processes. To add structure to these efforts, we derive new mathematical results to efficiently measure the changes in entropy as fully-connected and convolutional neural networks process data. By measuring the change in entropy as networks process data effectively, patterns critical to a well-performing network can be visualized and identified. Entropy-based loss terms are developed to improve dense and convolutional model accuracy and efficiency by promoting the ideal entropy patterns. Experiments in image compression, image classification, and image segmentation on benchmark datasets demonstrate these losses guide neural networks to learn rich latent data representations in fewer dimensions, converge in fewer training epochs, and achieve higher accuracy.
|
http://arxiv.org/pdf/2308.14938v2
|
[
"Mackenzie J. Meni",
"Ryan T. White",
"Michael Mayo",
"Kevin Pilkiewicz"
] |
2024-07-03T23:15:39Z
|
2023-08-28T23:33:07Z
|
2204.12440
|
Neuro-BERT: Rethinking Masked Autoencoding for Self-supervised
Neurological Pretraining
|
Deep learning associated with neurological signals is poised to drive major advancements in diverse fields such as medical diagnostics, neurorehabilitation, and brain-computer interfaces. The challenge in harnessing the full potential of these signals lies in the dependency on extensive, high-quality annotated data, which is often scarce and expensive to acquire, requiring specialized infrastructure and domain expertise. To address the appetite for data in deep learning, we present Neuro-BERT, a self-supervised pre-training framework of neurological signals based on masked autoencoding in the Fourier domain. The intuition behind our approach is simple: frequency and phase distribution of neurological signals can reveal intricate neurological activities. We propose a novel pre-training task dubbed Fourier Inversion Prediction (FIP), which randomly masks out a portion of the input signal and then predicts the missing information using the Fourier inversion theorem. Pre-trained models can be potentially used for various downstream tasks such as sleep stage classification and gesture recognition. Unlike contrastive-based methods, which strongly rely on carefully hand-crafted augmentations and siamese structure, our approach works reasonably well with a simple transformer encoder with no augmentation requirements. By evaluating our method on several benchmark datasets, we show that Neuro-BERT improves downstream neurological-related tasks by a large margin.
|
http://arxiv.org/pdf/2204.12440v2
|
[
"Di Wu",
"Siyuan Li",
"Jie Yang",
"Mohamad Sawan"
] |
2024-07-03T22:27:37Z
|
2022-04-20T16:48:18Z
|
2310.04444
|
What's the Magic Word? A Control Theory of LLM Prompting
|
Prompt engineering is crucial for deploying LLMs but is poorly understood mathematically. We formalize LLM systems as a class of discrete stochastic dynamical systems to explore prompt engineering through the lens of control theory. We offer a mathematical analysis of the limitations on the controllability of self-attention as a function of the singular values of the parameter matrices. We present complementary empirical results on the controllability of a panel of LLMs, including Falcon-7b, Llama-7b, and Falcon-40b. Given initial state $mathbf x_0$ from Wikitext and prompts of length $k leq 10$ tokens, we find that the "correct" next token is reachable at least 97% of the time, and that the top 75 most likely next tokens are reachable at least 85% of the time. Intriguingly, short prompt sequences can dramatically alter the likelihood of specific outputs, even making the least likely tokens become the most likely ones. This control-theoretic analysis of LLMs demonstrates the significant and poorly understood role of input sequences in steering output probabilities, offering a foundational perspective for enhancing language model system capabilities.
|
http://arxiv.org/pdf/2310.04444v4
|
[
"Aman Bhargava",
"Cameron Witkowski",
"Shi-Zhuo Looi",
"Matt Thomson"
] |
2024-07-03T22:23:50Z
|
2023-10-02T22:35:40Z
|
2407.03524
|
A multicategory jet image classification framework using deep neural
network
|
Jet point cloud images are high dimensional data structures that needs to be transformed to a separable feature space for machine learning algorithms to distinguish them with simple decision boundaries. In this article, the authors focus on jet category separability by particle and jet feature extraction, resulting in more efficient training of a simple deep neural network, resulting in a computational efficient interpretable model for jet classification. The methodology is tested with three to five categories of jets from the JetNet benchmark jet tagging dataset, resulting in comparable performance to particle flow network. This work demonstrates that high dimensional datasets represented in separable latent spaces lead to simpler architectures for jet classification.
|
http://arxiv.org/pdf/2407.03524v1
|
[
"Jairo Orozco Sandoval",
"Vidya Manian",
"Sudhir Malik"
] |
2024-07-03T22:00:35Z
|
2024-07-03T22:00:35Z
|
2407.03522
|
Optimal thresholds and algorithms for a model of multi-modal learning in
high dimensions
|
This work explores multi-modal inference in a high-dimensional simplified model, analytically quantifying the performance gain of multi-modal inference over that of analyzing modalities in isolation. We present the Bayes-optimal performance and weak recovery thresholds in a model where the objective is to recover the latent structures from two noisy data matrices with correlated spikes. The paper derives the approximate message passing (AMP) algorithm for this model and characterizes its performance in the high-dimensional limit via the associated state evolution. The analysis holds for a broad range of priors and noise channels, which can differ across modalities. The linearization of AMP is compared numerically to the widely used partial least squares (PLS) and canonical correlation analysis (CCA) methods, which are both observed to suffer from a sub-optimal recovery threshold.
|
http://arxiv.org/pdf/2407.03522v1
|
[
"Christian Keup",
"Lenka Zdeborová"
] |
2024-07-03T21:48:23Z
|
2024-07-03T21:48:23Z
|
2407.03515
|
Feature-Specific Coefficients of Determination in Tree Ensembles
|
Tree ensemble methods provide promising predictions with models difficult to interpret. Recent introduction of Shapley values for individualized feature contributions, accompanied with several fast computing algorithms for predicted values, shows intriguing results. However, individualizing coefficients of determination, aka $R^2$, for each feature is challenged by the underlying quadratic losses, although these coefficients allow us to comparatively assess single feature's contribution to tree ensembles. Here we propose an efficient algorithm, Q-SHAP, that reduces the computational complexity to polynomial time when calculating Shapley values related to quadratic losses. Our extensive simulation studies demonstrate that this approach not only enhances computational efficiency but also improves estimation accuracy of feature-specific coefficients of determination.
|
http://arxiv.org/pdf/2407.03515v1
|
[
"Zhongli Jiang",
"Dabao Zhang",
"Min Zhang"
] |
2024-07-03T21:27:29Z
|
2024-07-03T21:27:29Z
|
2406.03276
|
Revisiting Scalable Hessian Diagonal Approximations for Applications in
Reinforcement Learning
|
Second-order information is valuable for many applications but challenging to compute. Several works focus on computing or approximating Hessian diagonals, but even this simplification introduces significant additional costs compared to computing a gradient. In the absence of efficient exact computation schemes for Hessian diagonals, we revisit an early approximation scheme proposed by Becker and LeCun (1989, BL89), which has a cost similar to gradients and appears to have been overlooked by the community. We introduce HesScale, an improvement over BL89, which adds negligible extra computation. On small networks, we find that this improvement is of higher quality than all alternatives, even those with theoretical guarantees, such as unbiasedness, while being much cheaper to compute. We use this insight in reinforcement learning problems where small networks are used and demonstrate HesScale in second-order optimization and scaling the step-size parameter. In our experiments, HesScale optimizes faster than existing methods and improves stability through step-size scaling. These findings are promising for scaling second-order methods in larger models in the future.
|
http://arxiv.org/pdf/2406.03276v2
|
[
"Mohamed Elsayed",
"Homayoon Farrahi",
"Felix Dangel",
"A. Rupam Mahmood"
] |
2024-07-03T21:22:00Z
|
2024-06-05T13:53:20Z
|
2407.03502
|
AgentInstruct: Toward Generative Teaching with Agentic Flows
|
Synthetic data is becoming increasingly important for accelerating the development of language models, both large and small. Despite several successful use cases, researchers also raised concerns around model collapse and drawbacks of imitating other models. This discrepancy can be attributed to the fact that synthetic data varies in quality and diversity. Effective use of synthetic data usually requires significant human effort in curating the data. We focus on using synthetic data for post-training, specifically creating data by powerful models to teach a new skill or behavior to another model, we refer to this setting as Generative Teaching. We introduce AgentInstruct, an extensible agentic framework for automatically creating large amounts of diverse and high-quality synthetic data. AgentInstruct can create both the prompts and responses, using only raw data sources like text documents and code files as seeds. We demonstrate the utility of AgentInstruct by creating a post training dataset of 25M pairs to teach language models different skills, such as text editing, creative writing, tool usage, coding, reading comprehension, etc. The dataset can be used for instruction tuning of any base model. We post-train Mistral-7b with the data. When comparing the resulting model Orca-3 to Mistral-7b-Instruct (which uses the same base model), we observe significant improvements across many benchmarks. For example, 40% improvement on AGIEval, 19% improvement on MMLU, 54% improvement on GSM8K, 38% improvement on BBH and 45% improvement on AlpacaEval. Additionally, it consistently outperforms other models such as LLAMA-8B-instruct and GPT-3.5-turbo.
|
http://arxiv.org/pdf/2407.03502v1
|
[
"Arindam Mitra",
"Luciano Del Corro",
"Guoqing Zheng",
"Shweti Mahajan",
"Dany Rouhana",
"Andres Codas",
"Yadong Lu",
"Wei-ge Chen",
"Olga Vrousgos",
"Corby Rosset",
"Fillipe Silva",
"Hamed Khanpour",
"Yash Lara",
"Ahmed Awadallah"
] |
2024-07-03T21:01:12Z
|
2024-07-03T21:01:12Z
|
2407.03495
|
Codec-ASR: Training Performant Automatic Speech Recognition Systems with
Discrete Speech Representations
|
Discrete speech representations have garnered recent attention for their efficacy in training transformer-based models for various speech-related tasks such as automatic speech recognition (ASR), translation, speaker verification, and joint speech-text foundational models. In this work, we present a comprehensive analysis on building ASR systems with discrete codes. We investigate different methods for codec training such as quantization schemes and time-domain vs spectral feature encodings. We further explore ASR training techniques aimed at enhancing performance, training efficiency, and noise robustness. Drawing upon our findings, we introduce a codec ASR pipeline that outperforms Encodec at similar bit-rate. Remarkably, it also surpasses the state-of-the-art results achieved by strong self-supervised models on the 143 languages ML-SUPERB benchmark despite being smaller in size and pretrained on significantly less data.
|
http://arxiv.org/pdf/2407.03495v1
|
[
"Kunal Dhawan",
"Nithin Rao Koluguri",
"Ante Jukić",
"Ryan Langman",
"Jagadeesh Balam",
"Boris Ginsburg"
] |
2024-07-03T20:51:41Z
|
2024-07-03T20:51:41Z
|
2311.01375
|
Monotone Generative Modeling via a Gromov-Monge Embedding
|
Generative adversarial networks (GANs) are popular for generative tasks; however, they often require careful architecture selection, extensive empirical tuning, and are prone to mode collapse. To overcome these challenges, we propose a novel model that identifies the low-dimensional structure of the underlying data distribution, maps it into a low-dimensional latent space while preserving the underlying geometry, and then optimally transports a reference measure to the embedded distribution. We prove three key properties of our method: 1) The encoder preserves the geometry of the underlying data; 2) The generator is $c$-cyclically monotone, where $c$ is an intrinsic embedding cost employed by the encoder; and 3) The discriminator's modulus of continuity improves with the geometric preservation of the data. Numerical experiments demonstrate the effectiveness of our approach in generating high-quality images and exhibiting robustness to both mode collapse and training instability.
|
http://arxiv.org/pdf/2311.01375v2
|
[
"Wonjun Lee",
"Yifei Yang",
"Dongmian Zou",
"Gilad Lerman"
] |
2024-07-03T20:35:27Z
|
2023-11-02T16:33:35Z
|
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