arxiv_id
stringlengths 7
11
| title
stringlengths 7
243
| abstract
stringlengths 3
2.79k
| link
stringlengths 21
49
| authors
listlengths 1
451
| updated
stringlengths 20
20
| published
stringlengths 20
20
|
|---|---|---|---|---|---|---|
2112.04620
|
Online Calibrated and Conformal Prediction Improves Bayesian
Optimization
|
Accurate uncertainty estimates are important in sequential model-based decision-making tasks such as Bayesian optimization. However, these estimates can be imperfect if the data violates assumptions made by the model (e.g., Gaussianity). This paper studies which uncertainties are needed in model-based decision-making and in Bayesian optimization, and argues that uncertainties can benefit from calibration -- i.e., an 80% predictive interval should contain the true outcome 80% of the time. Maintaining calibration, however, can be challenging when the data is non-stationary and depends on our actions. We propose using simple algorithms based on online learning to provably maintain calibration on non-i.i.d. data, and we show how to integrate these algorithms in Bayesian optimization with minimal overhead. Empirically, we find that calibrated Bayesian optimization converges to better optima in fewer steps, and we demonstrate improved performance on standard benchmark functions and hyperparameter optimization tasks.
|
http://arxiv.org/pdf/2112.04620v5
|
[
"Shachi Deshpande",
"Charles Marx",
"Volodymyr Kuleshov"
] |
2024-06-25T20:14:27Z
|
2021-12-08T23:26:23Z
|
2404.18769
|
Learning with Norm Constrained, Over-parameterized, Two-layer Neural
Networks
|
Recent studies show that a reproducing kernel Hilbert space (RKHS) is not a suitable space to model functions by neural networks as the curse of dimensionality (CoD) cannot be evaded when trying to approximate even a single ReLU neuron (Bach, 2017). In this paper, we study a suitable function space for over-parameterized two-layer neural networks with bounded norms (e.g., the path norm, the Barron norm) in the perspective of sample complexity and generalization properties. First, we show that the path norm (as well as the Barron norm) is able to obtain width-independence sample complexity bounds, which allows for uniform convergence guarantees. Based on this result, we derive the improved result of metric entropy for $epsilon$-covering up to $O(epsilon^{-frac{2d}{d+2}})$ ($d$ is the input dimension and the depending constant is at most linear order of $d$) via the convex hull technique, which demonstrates the separation with kernel methods with $Omega(epsilon^{-d})$ to learn the target function in a Barron space. Second, this metric entropy result allows for building a sharper generalization bound under a general moment hypothesis setting, achieving the rate at $O(n^{-frac{d+2}{2d+2}})$. Our analysis is novel in that it offers a sharper and refined estimation for metric entropy with a linear dimension dependence and unbounded sampling in the estimation of the sample error and the output error.
|
http://arxiv.org/pdf/2404.18769v2
|
[
"Fanghui Liu",
"Leello Dadi",
"Volkan Cevher"
] |
2024-06-25T20:08:29Z
|
2024-04-29T15:04:07Z
|
2406.17916
|
Camera Model Identification Using Audio and Visual Content from Videos
|
The identification of device brands and models plays a pivotal role in the realm of multimedia forensic applications. This paper presents a framework capable of identifying devices using audio, visual content, or a fusion of them. The fusion of visual and audio content occurs later by applying two fundamental fusion rules: the product and the sum. The device identification problem is tackled as a classification one by leveraging Convolutional Neural Networks. Experimental evaluation illustrates that the proposed framework exhibits promising classification performance when independently using audio or visual content. Furthermore, although the fusion results don't consistently surpass both individual modalities, they demonstrate promising potential for enhancing classification performance. Future research could refine the fusion process to improve classification performance in both modalities consistently. Finally, a statistical significance test is performed for a more in-depth study of the classification results.
|
http://arxiv.org/pdf/2406.17916v1
|
[
"Ioannis Tsingalis",
"Christos Korgialas",
"Constantine Kotropoulos"
] |
2024-06-25T19:56:21Z
|
2024-06-25T19:56:21Z
|
2401.05972
|
Scientific Machine Learning Based Reduced-Order Models for Plasma
Turbulence Simulations
|
This paper focuses on the construction of non-intrusive Scientific Machine Learning (SciML) Reduced-Order Models (ROMs) for plasma turbulence simulations. In particular, we propose using Operator Inference (OpInf) to build low-cost physics-based ROMs from data for such simulations. As a representative example, we focus on the Hasegawa-Wakatani (HW) equations used for modeling two-dimensional electrostatic drift-wave turbulence. For a comprehensive perspective of the potential of OpInf to construct accurate ROMs, we consider three setups for the HW equations by varying a key model parameter, namely the adiabaticity coefficient. These setups lead to the formation of complex and nonlinear dynamics, which makes the construction of accurate ROMs of any kind challenging. We generate the training datasets by performing direct numerical simulations of the HW equations and recording the computed state data and outputs the over a time horizon of 100 time units in the turbulent phase. We then use these datasets to construct OpInf ROMs for predictions over 400 additional time units. Our results show that the OpInf ROMs capture the important features of the turbulent dynamics and generalize beyond the training time horizon while reducing the computational effort of the high-fidelity simulation by up to five orders of magnitude. In the broader context of fusion research, this shows that non-intrusive SciML ROMs have the potential to drastically accelerate numerical studies, which can ultimately enable tasks such as the design of optimized fusion devices.
|
http://arxiv.org/pdf/2401.05972v2
|
[
"Constantin Gahr",
"Ionut-Gabriel Farcas",
"Frank Jenko"
] |
2024-06-25T19:30:28Z
|
2024-01-11T15:20:06Z
|
2406.17902
|
Domain Adaptation of Echocardiography Segmentation Via Reinforcement
Learning
|
Performance of deep learning segmentation models is significantly challenged in its transferability across different medical imaging domains, particularly when aiming to adapt these models to a target domain with insufficient annotated data for effective fine-tuning. While existing domain adaptation (DA) methods propose strategies to alleviate this problem, these methods do not explicitly incorporate human-verified segmentation priors, compromising the potential of a model to produce anatomically plausible segmentations. We introduce RL4Seg, an innovative reinforcement learning framework that reduces the need to otherwise incorporate large expertly annotated datasets in the target domain, and eliminates the need for lengthy manual human review. Using a target dataset of 10,000 unannotated 2D echocardiographic images, RL4Seg not only outperforms existing state-of-the-art DA methods in accuracy but also achieves 99% anatomical validity on a subset of 220 expert-validated subjects from the target domain. Furthermore, our framework's reward network offers uncertainty estimates comparable with dedicated state-of-the-art uncertainty methods, demonstrating the utility and effectiveness of RL4Seg in overcoming domain adaptation challenges in medical image segmentation.
|
http://arxiv.org/pdf/2406.17902v1
|
[
"Arnaud Judge",
"Thierry Judge",
"Nicolas Duchateau",
"Roman A. Sandler",
"Joseph Z. Sokol",
"Olivier Bernard",
"Pierre-Marc Jodoin"
] |
2024-06-25T19:26:39Z
|
2024-06-25T19:26:39Z
|
2406.17899
|
Entity Augmentation for Efficient Classification of Vertically
Partitioned Data with Limited Overlap
|
Vertical Federated Learning (VFL) is a machine learning paradigm for learning from vertically partitioned data (i.e. features for each input are distributed across multiple "guest" clients and an aggregating "host" server owns labels) without communicating raw data. Traditionally, VFL involves an "entity resolution" phase where the host identifies and serializes the unique entities known to all guests. This is followed by private set intersection to find common entities, and an "entity alignment" step to ensure all guests are always processing the same entity's data. However, using only data of entities from the intersection means guests discard potentially useful data. Besides, the effect on privacy is dubious and these operations are computationally expensive. We propose a novel approach that eliminates the need for set intersection and entity alignment in categorical tasks. Our Entity Augmentation technique generates meaningful labels for activations sent to the host, regardless of their originating entity, enabling efficient VFL without explicit entity alignment. With limited overlap between training data, this approach performs substantially better (e.g. with 5% overlap, 48.1% vs 69.48% test accuracy on CIFAR-10). In fact, thanks to the regularizing effect, our model performs marginally better even with 100% overlap.
|
http://arxiv.org/pdf/2406.17899v1
|
[
"Avi Amalanshu",
"Viswesh Nagaswamy",
"G. V. S. S. Prudhvi",
"Yash Sirvi",
"Debashish Chakravarty"
] |
2024-06-25T19:20:10Z
|
2024-06-25T19:20:10Z
|
2406.17894
|
Efficient and Effective Implicit Dynamic Graph Neural Network
|
Implicit graph neural networks have gained popularity in recent years as they capture long-range dependencies while improving predictive performance in static graphs. Despite the tussle between performance degradation due to the oversmoothing of learned embeddings and long-range dependency being more pronounced in dynamic graphs, as features are aggregated both across neighborhood and time, no prior work has proposed an implicit graph neural model in a dynamic setting. In this paper, we present Implicit Dynamic Graph Neural Network (IDGNN) a novel implicit neural network for dynamic graphs which is the first of its kind. A key characteristic of IDGNN is that it demonstrably is well-posed, i.e., it is theoretically guaranteed to have a fixed-point representation. We then demonstrate that the standard iterative algorithm often used to train implicit models is computationally expensive in our dynamic setting as it involves computing gradients, which themselves have to be estimated in an iterative manner. To overcome this, we pose an equivalent bilevel optimization problem and propose an efficient single-loop training algorithm that avoids iterative computation by maintaining moving averages of key components of the gradients. We conduct extensive experiments on real-world datasets on both classification and regression tasks to demonstrate the superiority of our approach over the state-of-the-art baselines. We also demonstrate that our bi-level optimization framework maintains the performance of the expensive iterative algorithm while obtaining up to textbf{1600x} speed-up.
|
http://arxiv.org/pdf/2406.17894v1
|
[
"Yongjian Zhong",
"Hieu Vu",
"Tianbao Yang",
"Bijaya Adhikari"
] |
2024-06-25T19:07:21Z
|
2024-06-25T19:07:21Z
|
2405.15636
|
Visualize and Paint GAN Activations
|
We investigate how generated structures of GANs correlate with their activations in hidden layers, with the purpose of better understanding the inner workings of those models and being able to paint structures with unconditionally trained GANs. This gives us more control over the generated images, allowing to generate them from a semantic segmentation map while not requiring such a segmentation in the training data. To this end we introduce the concept of tileable features, allowing us to identify activations that work well for painting.
|
http://arxiv.org/pdf/2405.15636v2
|
[
"Rudolf Herdt",
"Peter Maass"
] |
2024-06-25T19:05:11Z
|
2024-05-24T15:22:58Z
|
2406.17890
|
SigKAN: Signature-Weighted Kolmogorov-Arnold Networks for Time Series
|
We propose a novel approach that enhances multivariate function approximation using learnable path signatures and Kolmogorov-Arnold networks (KANs). We enhance the learning capabilities of these networks by weighting the values obtained by KANs using learnable path signatures, which capture important geometric features of paths. This combination allows for a more comprehensive and flexible representation of sequential and temporal data. We demonstrate through studies that our SigKANs with learnable path signatures perform better than conventional methods across a range of function approximation challenges. By leveraging path signatures in neural networks, this method offers intriguing opportunities to enhance performance in time series analysis and time series forecasting, among other fields.
|
http://arxiv.org/pdf/2406.17890v1
|
[
"Hugo Inzirillo",
"Remi Genet"
] |
2024-06-25T18:58:39Z
|
2024-06-25T18:58:39Z
|
2406.17888
|
CTBench: A Comprehensive Benchmark for Evaluating Language Model
Capabilities in Clinical Trial Design
|
CTBench is introduced as a benchmark to assess language models (LMs) in aiding clinical study design. Given study-specific metadata, CTBench evaluates AI models' ability to determine the baseline features of a clinical trial (CT), which include demographic and relevant features collected at the trial's start from all participants. These baseline features, typically presented in CT publications (often as Table 1), are crucial for characterizing study cohorts and validating results. Baseline features, including confounders and covariates, are also necessary for accurate treatment effect estimation in studies involving observational data. CTBench consists of two datasets: "CT-Repo," containing baseline features from 1,690 clinical trials sourced from clinicaltrials.gov, and "CT-Pub," a subset of 100 trials with more comprehensive baseline features gathered from relevant publications. Two LM-based evaluation methods are developed to compare the actual baseline feature lists against LM-generated responses. "ListMatch-LM" and "ListMatch-BERT" use GPT-4o and BERT scores (at various thresholds), respectively, for evaluation. To establish baseline results, advanced prompt engineering techniques using LLaMa3-70B-Instruct and GPT-4o in zero-shot and three-shot learning settings are applied to generate potential baseline features. The performance of GPT-4o as an evaluator is validated through human-in-the-loop evaluations on the CT-Pub dataset, where clinical experts confirm matches between actual and LM-generated features. The results highlight a promising direction with significant potential for improvement, positioning CTBench as a useful tool for advancing research on AI in CT design and potentially enhancing the efficacy and robustness of CTs.
|
http://arxiv.org/pdf/2406.17888v1
|
[
"Nafis Neehal",
"Bowen Wang",
"Shayom Debopadhaya",
"Soham Dan",
"Keerthiram Murugesan",
"Vibha Anand",
"Kristin P. Bennett"
] |
2024-06-25T18:52:48Z
|
2024-06-25T18:52:48Z
|
2406.17887
|
Federated Dynamical Low-Rank Training with Global Loss Convergence
Guarantees
|
In this work, we propose a federated dynamical low-rank training (FeDLRT) scheme to reduce client compute and communication costs - two significant performance bottlenecks in horizontal federated learning. Our method builds upon dynamical low-rank splitting schemes for manifold-constrained optimization to create a global low-rank basis of network weights, which enables client training on a small coefficient matrix. A consistent global low-rank basis allows us to incorporate a variance correction scheme and prove global loss descent and convergence to a stationary point. Dynamic augmentation and truncation of the low-rank bases automatically optimizes computing and communication resource utilization. We demonstrate the efficiency of FeDLRT in an array of computer vision benchmarks and show a reduction of client compute and communication costs by up to an order of magnitude with minimal impacts on global accuracy.
|
http://arxiv.org/pdf/2406.17887v1
|
[
"Steffen Schotthöfer",
"M. Paul Laiu"
] |
2024-06-25T18:51:08Z
|
2024-06-25T18:51:08Z
|
2406.17885
|
Enabling Regional Explainability by Automatic and Model-agnostic Rule
Extraction
|
In Explainable AI, rule extraction translates model knowledge into logical rules, such as IF-THEN statements, crucial for understanding patterns learned by black-box models. This could significantly aid in fields like disease diagnosis, disease progression estimation, or drug discovery. However, such application domains often contain imbalanced data, with the class of interest underrepresented. Existing methods inevitably compromise the performance of rules for the minor class to maximise the overall performance. As the first attempt in this field, we propose a model-agnostic approach for extracting rules from specific subgroups of data, featuring automatic rule generation for numerical features. This method enhances the regional explainability of machine learning models and offers wider applicability compared to existing methods. We additionally introduce a new method for selecting features to compose rules, reducing computational costs in high-dimensional spaces. Experiments across various datasets and models demonstrate the effectiveness of our methods.
|
http://arxiv.org/pdf/2406.17885v1
|
[
"Yu Chen",
"Tianyu Cui",
"Alexander Capstick",
"Nan Fletcher-Loyd",
"Payam Barnaghi"
] |
2024-06-25T18:47:50Z
|
2024-06-25T18:47:50Z
|
2308.06873
|
SpeechX: Neural Codec Language Model as a Versatile Speech Transformer
|
Recent advancements in generative speech models based on audio-text prompts have enabled remarkable innovations like high-quality zero-shot text-to-speech. However, existing models still face limitations in handling diverse audio-text speech generation tasks involving transforming input speech and processing audio captured in adverse acoustic conditions. This paper introduces SpeechX, a versatile speech generation model capable of zero-shot TTS and various speech transformation tasks, dealing with both clean and noisy signals. SpeechX combines neural codec language modeling with multi-task learning using task-dependent prompting, enabling unified and extensible modeling and providing a consistent way for leveraging textual input in speech enhancement and transformation tasks. Experimental results show SpeechX's efficacy in various tasks, including zero-shot TTS, noise suppression, target speaker extraction, speech removal, and speech editing with or without background noise, achieving comparable or superior performance to specialized models across tasks. See https://aka.ms/speechx for demo samples.
|
http://arxiv.org/pdf/2308.06873v2
|
[
"Xiaofei Wang",
"Manthan Thakker",
"Zhuo Chen",
"Naoyuki Kanda",
"Sefik Emre Eskimez",
"Sanyuan Chen",
"Min Tang",
"Shujie Liu",
"Jinyu Li",
"Takuya Yoshioka"
] |
2024-06-25T18:38:28Z
|
2023-08-14T01:01:19Z
|
2406.17876
|
ET tu, CLIP? Addressing Common Object Errors for Unseen Environments
|
We introduce a simple method that employs pre-trained CLIP encoders to enhance model generalization in the ALFRED task. In contrast to previous literature where CLIP replaces the visual encoder, we suggest using CLIP as an additional module through an auxiliary object detection objective. We validate our method on the recently proposed Episodic Transformer architecture and demonstrate that incorporating CLIP improves task performance on the unseen validation set. Additionally, our analysis results support that CLIP especially helps with leveraging object descriptions, detecting small objects, and interpreting rare words.
|
http://arxiv.org/pdf/2406.17876v1
|
[
"Ye Won Byun",
"Cathy Jiao",
"Shahriar Noroozizadeh",
"Jimin Sun",
"Rosa Vitiello"
] |
2024-06-25T18:35:13Z
|
2024-06-25T18:35:13Z
|
2406.15647
|
Generating Music with Structure Using Self-Similarity as Attention
|
Despite the innovations in deep learning and generative AI, creating long term structure as well as the layers of repeated structure common in musical works remains an open challenge in music generation. We propose an attention layer that uses a novel approach applying user-supplied self-similarity matrices to previous time steps, and demonstrate it in our Similarity Incentivized Neural Generator (SING) system, a deep learning autonomous music generation system with two layers. The first is a vanilla Long Short Term Memory layer, and the second is the proposed attention layer. During generation, this attention mechanism imposes a suggested structure from a template piece on the generated music. We train SING on the MAESTRO dataset using a novel variable batching method, and compare its performance to the same model without the attention mechanism. The addition of our proposed attention mechanism significantly improves the network's ability to replicate specific structures, and it performs better on an unseen test set than a model without the attention mechanism.
|
http://arxiv.org/pdf/2406.15647v2
|
[
"Sophia Hager",
"Kathleen Hablutzel",
"Katherine M. Kinnaird"
] |
2024-06-25T18:26:07Z
|
2024-06-21T20:56:12Z
|
2307.04055
|
Contextual Dynamic Pricing with Strategic Buyers
|
Personalized pricing, which involves tailoring prices based on individual characteristics, is commonly used by firms to implement a consumer-specific pricing policy. In this process, buyers can also strategically manipulate their feature data to obtain a lower price, incurring certain manipulation costs. Such strategic behavior can hinder firms from maximizing their profits. In this paper, we study the contextual dynamic pricing problem with strategic buyers. The seller does not observe the buyer's true feature, but a manipulated feature according to buyers' strategic behavior. In addition, the seller does not observe the buyers' valuation of the product, but only a binary response indicating whether a sale happens or not. Recognizing these challenges, we propose a strategic dynamic pricing policy that incorporates the buyers' strategic behavior into the online learning to maximize the seller's cumulative revenue. We first prove that existing non-strategic pricing policies that neglect the buyers' strategic behavior result in a linear $Omega(T)$ regret with $T$ the total time horizon, indicating that these policies are not better than a random pricing policy. We then establish that our proposed policy achieves a sublinear regret upper bound of $O(sqrt{T})$. Importantly, our policy is not a mere amalgamation of existing dynamic pricing policies and strategic behavior handling algorithms. Our policy can also accommodate the scenario when the marginal cost of manipulation is unknown in advance. To account for it, we simultaneously estimate the valuation parameter and the cost parameter in the online pricing policy, which is shown to also achieve an $O(sqrt{T})$ regret bound. Extensive experiments support our theoretical developments and demonstrate the superior performance of our policy compared to other pricing policies that are unaware of the strategic behaviors.
|
http://arxiv.org/pdf/2307.04055v2
|
[
"Pangpang Liu",
"Zhuoran Yang",
"Zhaoran Wang",
"Will Wei Sun"
] |
2024-06-25T18:25:54Z
|
2023-07-08T23:06:42Z
|
2401.16549
|
Deep Learning for Multi-Label Learning: A Comprehensive Survey
|
Multi-label learning is a rapidly growing research area that aims to predict multiple labels from a single input data point. In the era of big data, tasks involving multi-label classification (MLC) or ranking present significant and intricate challenges, capturing considerable attention in diverse domains. Inherent difficulties in MLC include dealing with high-dimensional data, addressing label correlations, and handling partial labels, for which conventional methods prove ineffective. Recent years have witnessed a notable increase in adopting deep learning (DL) techniques to address these challenges more effectively in MLC. Notably, there is a burgeoning effort to harness the robust learning capabilities of DL for improved modelling of label dependencies and other challenges in MLC. However, it is noteworthy that comprehensive studies specifically dedicated to DL for multi-label learning are limited. Thus, this survey aims to thoroughly review recent progress in DL for multi-label learning, along with a summary of open research problems in MLC. The review consolidates existing research efforts in DL for MLC,including deep neural networks, transformers, autoencoders, and convolutional and recurrent architectures. Finally, the study presents a comparative analysis of the existing methods to provide insightful observations and stimulate future research directions in this domain.
|
http://arxiv.org/pdf/2401.16549v3
|
[
"Adane Nega Tarekegn",
"Mohib Ullah",
"Faouzi Alaya Cheikh"
] |
2024-06-25T18:20:40Z
|
2024-01-29T20:37:03Z
|
2404.02141
|
Robustly estimating heterogeneity in factorial data using Rashomon
Partitions
|
Many statistical analyses, in both observational data and randomized control trials, ask: how does the outcome of interest vary with combinations of observable covariates? How do various drug combinations affect health outcomes, or how does technology adoption depend on incentives and demographics? Our goal is to partition this factorial space into "pools" of covariate combinations where the outcome differs across the pools (but not within a pool). Existing approaches (i) search for a single "optimal" partition under assumptions about the association between covariates or (ii) sample from the entire set of possible partitions. Both these approaches ignore the reality that, especially with correlation structure in covariates, many ways to partition the covariate space may be statistically indistinguishable, despite very different implications for policy or science. We develop an alternative perspective, called Rashomon Partition Sets (RPSs). Each item in the RPS partitions the space of covariates using a tree-like geometry. RPSs incorporate all partitions that have posterior values near the maximum a posteriori partition, even if they offer substantively different explanations, and do so using a prior that makes no assumptions about associations between covariates. This prior is the $ell_0$ prior, which we show is minimax optimal. Given the RPS we calculate the posterior of any measurable function of the feature effects vector on outcomes, conditional on being in the RPS. We also characterize approximation error relative to the entire posterior and provide bounds on the size of the RPS. Simulations demonstrate this framework allows for robust conclusions relative to conventional regularization techniques. We apply our method to three empirical settings: price effects on charitable giving, chromosomal structure (telomere length), and the introduction of microfinance.
|
http://arxiv.org/pdf/2404.02141v2
|
[
"Aparajithan Venkateswaran",
"Anirudh Sankar",
"Arun G. Chandrasekhar",
"Tyler H. McCormick"
] |
2024-06-25T18:17:43Z
|
2024-04-02T17:53:28Z
|
2406.01274
|
Expected Grad-CAM: Towards gradient faithfulness
|
Although input-gradients techniques have evolved to mitigate and tackle the challenges associated with gradients, modern gradient-weighted CAM approaches still rely on vanilla gradients, which are inherently susceptible to the saturation phenomena. Despite recent enhancements have incorporated counterfactual gradient strategies as a mitigating measure, these local explanation techniques still exhibit a lack of sensitivity to their baseline parameter. Our work proposes a gradient-weighted CAM augmentation that tackles both the saturation and sensitivity problem by reshaping the gradient computation, incorporating two well-established and provably approaches: Expected Gradients and kernel smoothing. By revisiting the original formulation as the smoothed expectation of the perturbed integrated gradients, one can concurrently construct more faithful, localized and robust explanations which minimize infidelity. Through fine modulation of the perturbation distribution it is possible to regulate the complexity characteristic of the explanation, selectively discriminating stable features. Our technique, Expected Grad-CAM, differently from recent works, exclusively optimizes the gradient computation, purposefully designed as an enhanced substitute of the foundational Grad-CAM algorithm and any method built therefrom. Quantitative and qualitative evaluations have been conducted to assess the effectiveness of our method.
|
http://arxiv.org/pdf/2406.01274v2
|
[
"Vincenzo Buono",
"Peyman Sheikholharam Mashhadi",
"Mahmoud Rahat",
"Prayag Tiwari",
"Stefan Byttner"
] |
2024-06-25T18:10:15Z
|
2024-06-03T12:40:30Z
|
2406.17768
|
EXTRACT: Efficient Policy Learning by Extracting Transferrable Robot
Skills from Offline Data
|
Most reinforcement learning (RL) methods focus on learning optimal policies over low-level action spaces. While these methods can perform well in their training environments, they lack the flexibility to transfer to new tasks. Instead, RL agents that can act over useful, temporally extended skills rather than low-level actions can learn new tasks more easily. Prior work in skill-based RL either requires expert supervision to define useful skills, which is hard to scale, or learns a skill-space from offline data with heuristics that limit the adaptability of the skills, making them difficult to transfer during downstream RL. Our approach, EXTRACT, instead utilizes pre-trained vision language models to extract a discrete set of semantically meaningful skills from offline data, each of which is parameterized by continuous arguments, without human supervision. This skill parameterization allows robots to learn new tasks by only needing to learn when to select a specific skill and how to modify its arguments for the specific task. We demonstrate through experiments in sparse-reward, image-based, robot manipulation environments that EXTRACT can more quickly learn new tasks than prior works, with major gains in sample efficiency and performance over prior skill-based RL. Website at https://www.jessezhang.net/projects/extract/.
|
http://arxiv.org/pdf/2406.17768v1
|
[
"Jesse Zhang",
"Minho Heo",
"Zuxin Liu",
"Erdem Biyik",
"Joseph J Lim",
"Yao Liu",
"Rasool Fakoor"
] |
2024-06-25T17:50:03Z
|
2024-06-25T17:50:03Z
|
2206.08101
|
Towards Diverse Evaluation of Class Incremental Learning: A
Representation Learning Perspective
|
Class incremental learning (CIL) algorithms aim to continually learn new object classes from incrementally arriving data while not forgetting past learned classes. The common evaluation protocol for CIL algorithms is to measure the average test accuracy across all classes learned so far -- however, we argue that solely focusing on maximizing the test accuracy may not necessarily lead to developing a CIL algorithm that also continually learns and updates the representations, which may be transferred to the downstream tasks. To that end, we experimentally analyze neural network models trained by CIL algorithms using various evaluation protocols in representation learning and propose new analysis methods. Our experiments show that most state-of-the-art algorithms prioritize high stability and do not significantly change the learned representation, and sometimes even learn a representation of lower quality than a naive baseline. However, we observe that these algorithms can still achieve high test accuracy because they enable a model to learn a classifier that closely resembles an estimated linear classifier trained for linear probing. Furthermore, the base model learned in the first task, which involves single-task learning, exhibits varying levels of representation quality across different algorithms, and this variance impacts the final performance of CIL algorithms. Therefore, we suggest that the representation-level evaluation should be considered as an additional recipe for more diverse evaluation for CIL algorithms.
|
http://arxiv.org/pdf/2206.08101v3
|
[
"Sungmin Cha",
"Jihwan Kwak",
"Dongsub Shim",
"Hyunwoo Kim",
"Moontae Lee",
"Honglak Lee",
"Taesup Moon"
] |
2024-06-25T17:49:35Z
|
2022-06-16T11:44:11Z
|
2406.17763
|
DiffusionPDE: Generative PDE-Solving Under Partial Observation
|
We introduce a general framework for solving partial differential equations (PDEs) using generative diffusion models. In particular, we focus on the scenarios where we do not have the full knowledge of the scene necessary to apply classical solvers. Most existing forward or inverse PDE approaches perform poorly when the observations on the data or the underlying coefficients are incomplete, which is a common assumption for real-world measurements. In this work, we propose DiffusionPDE that can simultaneously fill in the missing information and solve a PDE by modeling the joint distribution of the solution and coefficient spaces. We show that the learned generative priors lead to a versatile framework for accurately solving a wide range of PDEs under partial observation, significantly outperforming the state-of-the-art methods for both forward and inverse directions.
|
http://arxiv.org/pdf/2406.17763v1
|
[
"Jiahe Huang",
"Guandao Yang",
"Zichen Wang",
"Jeong Joon Park"
] |
2024-06-25T17:48:24Z
|
2024-06-25T17:48:24Z
|
2406.17762
|
Solving Hard Mizar Problems with Instantiation and Strategy Invention
|
In this work, we prove over 3000 previously ATP-unproved Mizar/MPTP problems by using several ATP and AI methods, raising the number of ATP-solved Mizar problems from 75% to above 80%. First, we start to experiment with the cvc5 SMT solver which uses several instantiation-based heuristics that differ from the superposition-based systems, that were previously applied to Mizar,and add many new solutions. Then we use automated strategy invention to develop cvc5 strategies that largely improve cvc5's performance on the hard problems. In particular, the best invented strategy solves over 14% more problems than the best previously available cvc5 strategy. We also show that different clausification methods have a high impact on such instantiation-based methods, again producing many new solutions. In total, the methods solve 3021 (21.3%) of the 14163 previously unsolved hard Mizar problems. This is a new milestone over the Mizar large-theory benchmark and a large strengthening of the hammer methods for Mizar.
|
http://arxiv.org/pdf/2406.17762v1
|
[
"Jan Jakubův",
"Mikoláš Janota",
"Josef Urban"
] |
2024-06-25T17:47:13Z
|
2024-06-25T17:47:13Z
|
2406.06582
|
Discrete Multimodal Transformers with a Pretrained Large Language Model
for Mixed-Supervision Speech Processing
|
Recent work on discrete speech tokenization has paved the way for models that can seamlessly perform multiple tasks across modalities, e.g., speech recognition, text to speech, speech to speech translation. Moreover, large language models (LLMs) pretrained from vast text corpora contain rich linguistic information that can improve accuracy in a variety of tasks. In this paper, we present a decoder-only Discrete Multimodal Language Model (DMLM), which can be flexibly applied to multiple tasks (ASR, T2S, S2TT, etc.) and modalities (text, speech, vision). We explore several critical aspects of discrete multi-modal models, including the loss function, weight initialization, mixed training supervision, and codebook. Our results show that DMLM benefits significantly, across multiple tasks and datasets, from a combination of supervised and unsupervised training. Moreover, for ASR, it benefits from initializing DMLM from a pretrained LLM, and from a codebook derived from Whisper activations.
|
http://arxiv.org/pdf/2406.06582v2
|
[
"Viet Anh Trinh",
"Rosy Southwell",
"Yiwen Guan",
"Xinlu He",
"Zhiyong Wang",
"Jacob Whitehill"
] |
2024-06-25T17:44:00Z
|
2024-06-04T20:08:25Z
|
2406.17759
|
Interpreting Attention Layer Outputs with Sparse Autoencoders
|
Decomposing model activations into interpretable components is a key open problem in mechanistic interpretability. Sparse autoencoders (SAEs) are a popular method for decomposing the internal activations of trained transformers into sparse, interpretable features, and have been applied to MLP layers and the residual stream. In this work we train SAEs on attention layer outputs and show that also here SAEs find a sparse, interpretable decomposition. We demonstrate this on transformers from several model families and up to 2B parameters. We perform a qualitative study of the features computed by attention layers, and find multiple families: long-range context, short-range context and induction features. We qualitatively study the role of every head in GPT-2 Small, and estimate that at least 90% of the heads are polysemantic, i.e. have multiple unrelated roles. Further, we show that Sparse Autoencoders are a useful tool that enable researchers to explain model behavior in greater detail than prior work. For example, we explore the mystery of why models have so many seemingly redundant induction heads, use SAEs to motivate the hypothesis that some are long-prefix whereas others are short-prefix, and confirm this with more rigorous analysis. We use our SAEs to analyze the computation performed by the Indirect Object Identification circuit (Wang et al.), validating that the SAEs find causally meaningful intermediate variables, and deepening our understanding of the semantics of the circuit. We open-source the trained SAEs and a tool for exploring arbitrary prompts through the lens of Attention Output SAEs.
|
http://arxiv.org/pdf/2406.17759v1
|
[
"Connor Kissane",
"Robert Krzyzanowski",
"Joseph Isaac Bloom",
"Arthur Conmy",
"Neel Nanda"
] |
2024-06-25T17:43:13Z
|
2024-06-25T17:43:13Z
|
2309.13692
|
Regularization and Optimal Multiclass Learning
|
The quintessential learning algorithm of empirical risk minimization (ERM) is known to fail in various settings for which uniform convergence does not characterize learning. It is therefore unsurprising that the practice of machine learning is rife with considerably richer algorithmic techniques for successfully controlling model capacity. Nevertheless, no such technique or principle has broken away from the pack to characterize optimal learning in these more general settings. The purpose of this work is to characterize the role of regularization in perhaps the simplest setting for which ERM fails: multiclass learning with arbitrary label sets. Using one-inclusion graphs (OIGs), we exhibit optimal learning algorithms that dovetail with tried-and-true algorithmic principles: Occam's Razor as embodied by structural risk minimization (SRM), the principle of maximum entropy, and Bayesian reasoning. Most notably, we introduce an optimal learner which relaxes structural risk minimization on two dimensions: it allows the regularization function to be "local" to datapoints, and uses an unsupervised learning stage to learn this regularizer at the outset. We justify these relaxations by showing that they are necessary: removing either dimension fails to yield a near-optimal learner. We also extract from OIGs a combinatorial sequence we term the Hall complexity, which is the first to characterize a problem's transductive error rate exactly. Lastly, we introduce a generalization of OIGs and the transductive learning setting to the agnostic case, where we show that optimal orientations of Hamming graphs -- judged using nodes' outdegrees minus a system of node-dependent credits -- characterize optimal learners exactly. We demonstrate that an agnostic version of the Hall complexity again characterizes error rates exactly, and exhibit an optimal learner using maximum entropy programs.
|
http://arxiv.org/pdf/2309.13692v2
|
[
"Julian Asilis",
"Siddartha Devic",
"Shaddin Dughmi",
"Vatsal Sharan",
"Shang-Hua Teng"
] |
2024-06-25T17:42:18Z
|
2023-09-24T16:49:55Z
|
2405.13285
|
Enhancing Active Learning for Sentinel 2 Imagery through Contrastive
Learning and Uncertainty Estimation
|
In this paper, we introduce a novel method designed to enhance label efficiency in satellite imagery analysis by integrating semi-supervised learning (SSL) with active learning strategies. Our approach utilizes contrastive learning together with uncertainty estimations via Monte Carlo Dropout (MC Dropout), with a particular focus on Sentinel-2 imagery analyzed using the Eurosat dataset. We explore the effectiveness of our method in scenarios featuring both balanced and unbalanced class distributions. Our results show that the proposed method performs better than several other popular methods in this field, enabling significant savings in labeling effort while maintaining high classification accuracy. These findings highlight the potential of our approach to facilitate scalable and cost-effective satellite image analysis, particularly advantageous for extensive environmental monitoring and land use classification tasks.
|
http://arxiv.org/pdf/2405.13285v2
|
[
"David Pogorzelski",
"Peter Arlinghaus",
"Wenyan Zhang"
] |
2024-06-25T17:40:35Z
|
2024-05-22T01:54:51Z
|
2406.17749
|
Benchmarking Deep Learning Models on NVIDIA Jetson Nano for Real-Time
Systems: An Empirical Investigation
|
The proliferation of complex deep learning (DL) models has revolutionized various applications, including computer vision-based solutions, prompting their integration into real-time systems. However, the resource-intensive nature of these models poses challenges for deployment on low-computational power and low-memory devices, like embedded and edge devices. This work empirically investigates the optimization of such complex DL models to analyze their functionality on an embedded device, particularly on the NVIDIA Jetson Nano. It evaluates the effectiveness of the optimized models in terms of their inference speed for image classification and video action detection. The experimental results reveal that, on average, optimized models exhibit a 16.11% speed improvement over their non-optimized counterparts. This not only emphasizes the critical need to consider hardware constraints and environmental sustainability in model development and deployment but also underscores the pivotal role of model optimization in enabling the widespread deployment of AI-assisted technologies on resource-constrained computational systems. It also serves as proof that prioritizing hardware-specific model optimization leads to efficient and scalable solutions that substantially decrease energy consumption and carbon footprint.
|
http://arxiv.org/pdf/2406.17749v1
|
[
"Tushar Prasanna Swaminathan",
"Christopher Silver",
"Thangarajah Akilan"
] |
2024-06-25T17:34:52Z
|
2024-06-25T17:34:52Z
|
2406.17748
|
A New Perspective on Shampoo's Preconditioner
|
Shampoo, a second-order optimization algorithm which uses a Kronecker product preconditioner, has recently garnered increasing attention from the machine learning community. The preconditioner used by Shampoo can be viewed either as an approximation of the Gauss--Newton component of the Hessian or the covariance matrix of the gradients maintained by Adagrad. We provide an explicit and novel connection between the $textit{optimal}$ Kronecker product approximation of these matrices and the approximation made by Shampoo. Our connection highlights a subtle but common misconception about Shampoo's approximation. In particular, the $textit{square}$ of the approximation used by the Shampoo optimizer is equivalent to a single step of the power iteration algorithm for computing the aforementioned optimal Kronecker product approximation. Across a variety of datasets and architectures we empirically demonstrate that this is close to the optimal Kronecker product approximation. Additionally, for the Hessian approximation viewpoint, we empirically study the impact of various practical tricks to make Shampoo more computationally efficient (such as using the batch gradient and the empirical Fisher) on the quality of Hessian approximation.
|
http://arxiv.org/pdf/2406.17748v1
|
[
"Depen Morwani",
"Itai Shapira",
"Nikhil Vyas",
"Eran Malach",
"Sham Kakade",
"Lucas Janson"
] |
2024-06-25T17:34:51Z
|
2024-06-25T17:34:51Z
|
2406.17747
|
Probing the effects of broken symmetries in machine learning
|
Symmetry is one of the most central concepts in physics, and it is no surprise that it has also been widely adopted as an inductive bias for machine-learning models applied to the physical sciences. This is especially true for models targeting the properties of matter at the atomic scale. Both established and state-of-the-art approaches, with almost no exceptions, are built to be exactly equivariant to translations, permutations, and rotations of the atoms. Incorporating symmetries -- rotations in particular -- constrains the model design space and implies more complicated architectures that are often also computationally demanding. There are indications that non-symmetric models can easily learn symmetries from data, and that doing so can even be beneficial for the accuracy of the model. We put a model that obeys rotational invariance only approximately to the test, in realistic scenarios involving simulations of gas-phase, liquid, and solid water. We focus specifically on physical observables that are likely to be affected -- directly or indirectly -- by symmetry breaking, finding negligible consequences when the model is used in an interpolative, bulk, regime. Even for extrapolative gas-phase predictions, the model remains very stable, even though symmetry artifacts are noticeable. We also discuss strategies that can be used to systematically reduce the magnitude of symmetry breaking when it occurs, and assess their impact on the convergence of observables.
|
http://arxiv.org/pdf/2406.17747v1
|
[
"Marcel F. Langer",
"Sergey N. Pozdnyakov",
"Michele Ceriotti"
] |
2024-06-25T17:34:09Z
|
2024-06-25T17:34:09Z
|
2401.09384
|
Diverse Part Synthesis for 3D Shape Creation
|
Methods that use neural networks for synthesizing 3D shapes in the form of a part-based representation have been introduced over the last few years. These methods represent shapes as a graph or hierarchy of parts and enable a variety of applications such as shape sampling and reconstruction. However, current methods do not allow easily regenerating individual shape parts according to user preferences. In this paper, we investigate techniques that allow the user to generate multiple, diverse suggestions for individual parts. Specifically, we experiment with multimodal deep generative models that allow sampling diverse suggestions for shape parts and focus on models which have not been considered in previous work on shape synthesis. To provide a comparative study of these techniques, we introduce a method for synthesizing 3D shapes in a part-based representation and evaluate all the part suggestion techniques within this synthesis method. In our method, which is inspired by previous work, shapes are represented as a set of parts in the form of implicit functions which are then positioned in space to form the final shape. Synthesis in this representation is enabled by a neural network architecture based on an implicit decoder and a spatial transformer. We compare the various multimodal generative models by evaluating their performance in generating part suggestions. Our contribution is to show with qualitative and quantitative evaluations which of the new techniques for multimodal part generation perform the best and that a synthesis method based on the top-performing techniques allows the user to more finely control the parts that are generated in the 3D shapes while maintaining high shape fidelity when reconstructing shapes.
|
http://arxiv.org/pdf/2401.09384v2
|
[
"Yanran Guan",
"Oliver van Kaick"
] |
2024-06-25T17:33:31Z
|
2024-01-17T17:55:06Z
|
2406.17740
|
Structured Unrestricted-Rank Matrices for Parameter Efficient
Fine-tuning
|
Recent efforts to scale Transformer models have demonstrated rapid progress across a wide range of tasks (Wei et al., 2022). However, fine-tuning these models for downstream tasks is expensive due to their large parameter counts. Parameter-efficient fine-tuning (PEFT) approaches have emerged as a viable alternative by allowing us to fine-tune models by updating only a small number of parameters. In this work, we propose a general framework for parameter efficient fine-tuning (PEFT), based on structured unrestricted-rank matrices (SURM) which can serve as a drop-in replacement for popular approaches such as Adapters and LoRA. Unlike other methods like LoRA, SURMs provides more flexibility in finding the right balance between compactness and expressiveness. This is achieved by using low displacement rank matrices (LDRMs), which hasn't been used in this context before. SURMs remain competitive with baselines, often providing significant quality improvements while using a smaller parameter budget. SURMs achieve 5-7% accuracy gains on various image classification tasks while replacing low-rank matrices in LoRA. It also results in up to 12x reduction of the number of parameters in adapters (with virtually no loss in quality) on the GLUE benchmark.
|
http://arxiv.org/pdf/2406.17740v1
|
[
"Arijit Sehanobish",
"Avinava Dubey",
"Krzysztof Choromanski",
"Somnath Basu Roy Chowdhury",
"Deepali Jain",
"Vikas Sindhwani",
"Snigdha Chaturvedi"
] |
2024-06-25T17:26:05Z
|
2024-06-25T17:26:05Z
|
2406.17737
|
LLM Targeted Underperformance Disproportionately Impacts Vulnerable
Users
|
While state-of-the-art Large Language Models (LLMs) have shown impressive performance on many tasks, there has been extensive research on undesirable model behavior such as hallucinations and bias. In this work, we investigate how the quality of LLM responses changes in terms of information accuracy, truthfulness, and refusals depending on three user traits: English proficiency, education level, and country of origin. We present extensive experimentation on three state-of-the-art LLMs and two different datasets targeting truthfulness and factuality. Our findings suggest that undesirable behaviors in state-of-the-art LLMs occur disproportionately more for users with lower English proficiency, of lower education status, and originating from outside the US, rendering these models unreliable sources of information towards their most vulnerable users.
|
http://arxiv.org/pdf/2406.17737v1
|
[
"Elinor Poole-Dayan",
"Deb Roy",
"Jad Kabbara"
] |
2024-06-25T17:24:07Z
|
2024-06-25T17:24:07Z
|
2309.03145
|
The Best Arm Evades: Near-optimal Multi-pass Streaming Lower Bounds for
Pure Exploration in Multi-armed Bandits
|
We give a near-optimal sample-pass trade-off for pure exploration in multi-armed bandits (MABs) via multi-pass streaming algorithms: any streaming algorithm with sublinear memory that uses the optimal sample complexity of $O(frac{n}{Delta^2})$ requires $Omega(frac{log{(1/Delta)}}{loglog{(1/Delta)}})$ passes. Here, $n$ is the number of arms and $Delta$ is the reward gap between the best and the second-best arms. Our result matches the $O(log(frac{1}{Delta}))$-pass algorithm of Jin et al. [ICML'21] (up to lower order terms) that only uses $O(1)$ memory and answers an open question posed by Assadi and Wang [STOC'20].
|
http://arxiv.org/pdf/2309.03145v2
|
[
"Sepehr Assadi",
"Chen Wang"
] |
2024-06-25T17:20:06Z
|
2023-09-06T16:41:41Z
|
2401.10748
|
Fast gradient-free activation maximization for neurons in spiking neural
networks
|
Elements of neural networks, both biological and artificial, can be described by their selectivity for specific cognitive features. Understanding these features is important for understanding the inner workings of neural networks. For a living system, such as a neuron, whose response to a stimulus is unknown and not differentiable, the only way to reveal these features is through a feedback loop that exposes it to a large set of different stimuli. The properties of these stimuli should be varied iteratively in order to maximize the neuronal response. To utilize this feedback loop for a biological neural network, it is important to run it quickly and efficiently in order to reach the stimuli that maximizes certain neurons' activation with the least number of iterations possible. Here we present a framework with an efficient design for such a loop. We successfully tested it on an artificial spiking neural network (SNN), which is a model that simulates the asynchronous spiking activity of neurons in living brains. Our optimization method for activation maximization is based on the low-rank Tensor Train decomposition of the discrete activation function. The optimization space is the latent parameter space of images generated by SN-GAN or VQ-VAE generative models. To our knowledge, this is the first time that effective AM has been applied to SNNs. We track changes in the optimal stimuli for artificial neurons during training and show that highly selective neurons can form already in the early epochs of training and in the early layers of a convolutional spiking network. This formation of refined optimal stimuli is associated with an increase in classification accuracy. Some neurons, especially in the deeper layers, may gradually change the concepts they are selective for during learning, potentially explaining their importance for model performance.
|
http://arxiv.org/pdf/2401.10748v2
|
[
"Nikita Pospelov",
"Andrei Chertkov",
"Maxim Beketov",
"Ivan Oseledets",
"Konstantin Anokhin"
] |
2024-06-25T17:08:56Z
|
2023-12-28T18:30:13Z
|
2406.18613
|
Inducing Riesz and orthonormal bases in $L^2$ via composition operators
|
We investigate perturbations of orthonormal bases of $L^2$ via a composition operator $C_h$ induced by a mapping $h$. We provide a comprehensive characterization of the mapping $h$ required for the perturbed sequence to form an orthonormal or Riesz basis. Restricting our analysis to differentiable mappings, we reveal that all Riesz bases of the given form are induced by bi-Lipschitz mappings. In addition, we discuss implications of these results for approximation theory, highlighting the potential of using bijective neural networks to construct complete sequences with favorable approximation properties.
|
http://arxiv.org/pdf/2406.18613v1
|
[
"Yahya Saleh",
"Armin Iske"
] |
2024-06-25T17:07:01Z
|
2024-06-25T17:07:01Z
|
2406.17718
|
When does Self-Prediction help? Understanding Auxiliary Tasks in
Reinforcement Learning
|
We investigate the impact of auxiliary learning tasks such as observation reconstruction and latent self-prediction on the representation learning problem in reinforcement learning. We also study how they interact with distractions and observation functions in the MDP. We provide a theoretical analysis of the learning dynamics of observation reconstruction, latent self-prediction, and TD learning in the presence of distractions and observation functions under linear model assumptions. With this formalization, we are able to explain why latent-self prediction is a helpful emph{auxiliary task}, while observation reconstruction can provide more useful features when used in isolation. Our empirical analysis shows that the insights obtained from our learning dynamics framework predicts the behavior of these loss functions beyond the linear model assumption in non-linear neural networks. This reinforces the usefulness of the linear model framework not only for theoretical analysis, but also practical benefit for applied problems.
|
http://arxiv.org/pdf/2406.17718v1
|
[
"Claas Voelcker",
"Tyler Kastner",
"Igor Gilitschenski",
"Amir-massoud Farahmand"
] |
2024-06-25T17:06:57Z
|
2024-06-25T17:06:57Z
|
2402.14169
|
A Temporal Stochastic Bias Correction using a Machine Learning Attention
model
|
Climate models are biased with respect to real-world observations. They usually need to be adjusted before being used in impact studies. The suite of statistical methods that enable such adjustments is called bias correction (BC). However, BC methods currently struggle to adjust temporal biases. Because they mostly disregard the dependence between consecutive time points. As a result, climate statistics with long-range temporal properties, such as heatwave duration and frequency, cannot be corrected accurately. This makes it more difficult to produce reliable impact studies on such climate statistics. This paper offers a novel BC methodology to correct temporal biases. This is made possible by rethinking the philosophy behind BC. We will introduce BC as a time-indexed regression task with stochastic outputs. Rethinking BC enables us to adapt state-of-the-art machine learning (ML) attention models and thereby learn different types of biases, including temporal asynchronicities. With a case study of heatwave duration statistics in Abuja, Nigeria, and Tokyo, Japan, we show more accurate results than current climate model outputs and alternative BC methods.
|
http://arxiv.org/pdf/2402.14169v5
|
[
"Omer Nivron",
"Damon J. Wischik",
"Mathieu Vrac",
"Emily Shuckburgh",
"Alex T. Archibald"
] |
2024-06-25T17:03:22Z
|
2024-02-21T23:18:03Z
|
2402.15422
|
A Data-Centric Approach To Generate Faithful and High Quality Patient
Summaries with Large Language Models
|
Patients often face difficulties in understanding their hospitalizations, while healthcare workers have limited resources to provide explanations. In this work, we investigate the potential of large language models to generate patient summaries based on doctors' notes and study the effect of training data on the faithfulness and quality of the generated summaries. To this end, we release (i) a rigorous labeling protocol for errors in medical texts and (ii) a publicly available dataset of annotated hallucinations in 100 doctor-written and 100 generated summaries. We show that fine-tuning on hallucination-free data effectively reduces hallucinations from 2.60 to 1.55 per summary for Llama 2, while preserving relevant information. We observe a similar effect on GPT-4 (0.70 to 0.40), when the few-shot examples are hallucination-free. We also conduct a qualitative evaluation using hallucination-free and improved training data. We find that common quantitative metrics do not correlate well with faithfulness and quality. Finally, we test GPT-4 for automatic hallucination detection, which clearly outperforms common baselines.
|
http://arxiv.org/pdf/2402.15422v2
|
[
"Stefan Hegselmann",
"Shannon Zejiang Shen",
"Florian Gierse",
"Monica Agrawal",
"David Sontag",
"Xiaoyi Jiang"
] |
2024-06-25T17:02:10Z
|
2024-02-23T16:32:28Z
|
2312.03806
|
XCube: Large-Scale 3D Generative Modeling using Sparse Voxel Hierarchies
|
We present XCube (abbreviated as $mathcal{X}^3$), a novel generative model for high-resolution sparse 3D voxel grids with arbitrary attributes. Our model can generate millions of voxels with a finest effective resolution of up to $1024^3$ in a feed-forward fashion without time-consuming test-time optimization. To achieve this, we employ a hierarchical voxel latent diffusion model which generates progressively higher resolution grids in a coarse-to-fine manner using a custom framework built on the highly efficient VDB data structure. Apart from generating high-resolution objects, we demonstrate the effectiveness of XCube on large outdoor scenes at scales of 100m$times$100m with a voxel size as small as 10cm. We observe clear qualitative and quantitative improvements over past approaches. In addition to unconditional generation, we show that our model can be used to solve a variety of tasks such as user-guided editing, scene completion from a single scan, and text-to-3D. The source code and more results can be found at https://research.nvidia.com/labs/toronto-ai/xcube/.
|
http://arxiv.org/pdf/2312.03806v2
|
[
"Xuanchi Ren",
"Jiahui Huang",
"Xiaohui Zeng",
"Ken Museth",
"Sanja Fidler",
"Francis Williams"
] |
2024-06-25T17:01:54Z
|
2023-12-06T16:23:26Z
|
2406.17838
|
InFiConD: Interactive No-code Fine-tuning with Concept-based Knowledge
Distillation
|
The emergence of large-scale pre-trained models has heightened their application in various downstream tasks, yet deployment is a challenge in environments with limited computational resources. Knowledge distillation has emerged as a solution in such scenarios, whereby knowledge from large teacher models is transferred into smaller student' models, but this is a non-trivial process that traditionally requires technical expertise in AI/ML. To address these challenges, this paper presents InFiConD, a novel framework that leverages visual concepts to implement the knowledge distillation process and enable subsequent no-code fine-tuning of student models. We develop a novel knowledge distillation pipeline based on extracting text-aligned visual concepts from a concept corpus using multimodal models, and construct highly interpretable linear student models based on visual concepts that mimic a teacher model in a response-based manner. InFiConD's interface allows users to interactively fine-tune the student model by manipulating concept influences directly in the user interface. We validate InFiConD via a robust usage scenario and user study. Our findings indicate that InFiConD's human-in-the-loop and visualization-driven approach enables users to effectively create and analyze student models, understand how knowledge is transferred, and efficiently perform fine-tuning operations. We discuss how this work highlights the potential of interactive and visual methods in making knowledge distillation and subsequent no-code fine-tuning more accessible and adaptable to a wider range of users with domain-specific demands.
|
http://arxiv.org/pdf/2406.17838v1
|
[
"Jinbin Huang",
"Wenbin He",
"Liang Gou",
"Liu Ren",
"Chris Bryan"
] |
2024-06-25T16:56:45Z
|
2024-06-25T16:56:45Z
|
2406.17714
|
Compositional Models for Estimating Causal Effects
|
Many real-world systems can be represented as sets of interacting components. Examples of such systems include computational systems such as query processors, natural systems such as cells, and social systems such as families. Many approaches have been proposed in traditional (associational) machine learning to model such structured systems, including statistical relational models and graph neural networks. Despite this prior work, existing approaches to estimating causal effects typically treat such systems as single units, represent them with a fixed set of variables and assume a homogeneous data-generating process. We study a compositional approach for estimating individual treatment effects (ITE) in structured systems, where each unit is represented by the composition of multiple heterogeneous components. This approach uses a modular architecture to model potential outcomes at each component and aggregates component-level potential outcomes to obtain the unit-level potential outcomes. We discover novel benefits of the compositional approach in causal inference - systematic generalization to estimate counterfactual outcomes of unseen combinations of components and improved overlap guarantees between treatment and control groups compared to the classical methods for causal effect estimation. We also introduce a set of novel environments for empirically evaluating the compositional approach and demonstrate the effectiveness of our approach using both simulated and real-world data.
|
http://arxiv.org/pdf/2406.17714v1
|
[
"Purva Pruthi",
"David Jensen"
] |
2024-06-25T16:56:17Z
|
2024-06-25T16:56:17Z
|
2406.17711
|
Data curation via joint example selection further accelerates multimodal
learning
|
Data curation is an essential component of large-scale pretraining. In this work, we demonstrate that jointly selecting batches of data is more effective for learning than selecting examples independently. Multimodal contrastive objectives expose the dependencies between data and thus naturally yield criteria for measuring the joint learnability of a batch. We derive a simple and tractable algorithm for selecting such batches, which significantly accelerate training beyond individually-prioritized data points. As performance improves by selecting from larger super-batches, we also leverage recent advances in model approximation to reduce the associated computational overhead. As a result, our approach--multimodal contrastive learning with joint example selection (JEST)--surpasses state-of-the-art models with up to 13$times$ fewer iterations and 10$times$ less computation. Essential to the performance of JEST is the ability to steer the data selection process towards the distribution of smaller, well-curated datasets via pretrained reference models, exposing the level of data curation as a new dimension for neural scaling laws.
|
http://arxiv.org/pdf/2406.17711v1
|
[
"Talfan Evans",
"Nikhil Parthasarathy",
"Hamza Merzic",
"Olivier J. Henaff"
] |
2024-06-25T16:52:37Z
|
2024-06-25T16:52:37Z
|
2406.17706
|
FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Model
|
Large language models (LLMs) show amazing performance on many domain-specific tasks after fine-tuning with some appropriate data. However, many domain-specific data are privately distributed across multiple owners. Thus, this dilemma raises the interest in how to perform LLM fine-tuning in federated learning (FL). However, confronted with limited computation and communication capacities, FL clients struggle to fine-tune an LLM effectively. To this end, we introduce FedBiOT, a resource-efficient LLM fine-tuning approach to FL. Specifically, our method involves the server generating a compressed LLM and aligning its performance with the full model. Subsequently, the clients fine-tune a lightweight yet important part of the compressed model, referred to as an adapter. Notice that as the server has no access to the private data owned by the clients, the data used for alignment by the server has a different distribution from the one used for fine-tuning by clients. We formulate the problem into a bi-level optimization problem to minimize the negative effect of data discrepancy and derive the updating rules for the server and clients. We conduct extensive experiments on LLaMA-2, empirically showing that the adapter has exceptional performance when reintegrated into the global LLM. The results also indicate that the proposed FedBiOT significantly reduces resource consumption compared to existing benchmarks, all while achieving comparable performance levels.
|
http://arxiv.org/pdf/2406.17706v1
|
[
"Feijie Wu",
"Zitao Li",
"Yaliang Li",
"Bolin Ding",
"Jing Gao"
] |
2024-06-25T16:45:47Z
|
2024-06-25T16:45:47Z
|
2406.17699
|
Can independent Metropolis beat crude Monte Carlo?
|
Assume that we would like to estimate the expected value of a function $F$ with respect to a density $pi$. We prove that if $pi$ is close enough under KL divergence to another density $q$, an independent Metropolis sampler estimator that obtains samples from $pi$ with proposal density $q$, enriched with a variance reduction computational strategy based on control variates, achieves smaller asymptotic variance than that of the crude Monte Carlo estimator. The control variates construction requires no extra computational effort but assumes that the expected value of $F$ under $q$ is analytically available. We illustrate this result by calculating the marginal likelihood in a linear regression model with prior-likelihood conflict and a non-conjugate prior. Furthermore, we propose an adaptive independent Metropolis algorithm that adapts the proposal density such that its KL divergence with the target is being reduced. We demonstrate its applicability in a Bayesian logistic and Gaussian process regression problems and we rigorously justify our asymptotic arguments under easily verifiable and essentially minimal conditions.
|
http://arxiv.org/pdf/2406.17699v1
|
[
"Siran Liu",
"Petros Dellaportas",
"Michalis K. Titsias"
] |
2024-06-25T16:38:53Z
|
2024-06-25T16:38:53Z
|
2406.17698
|
Identifying Nonstationary Causal Structures with High-Order Markov
Switching Models
|
Causal discovery in time series is a rapidly evolving field with a wide variety of applications in other areas such as climate science and neuroscience. Traditional approaches assume a stationary causal graph, which can be adapted to nonstationary time series with time-dependent effects or heterogeneous noise. In this work we address nonstationarity via regime-dependent causal structures. We first establish identifiability for high-order Markov Switching Models, which provide the foundations for identifiable regime-dependent causal discovery. Our empirical studies demonstrate the scalability of our proposed approach for high-order regime-dependent structure estimation, and we illustrate its applicability on brain activity data.
|
http://arxiv.org/pdf/2406.17698v1
|
[
"Carles Balsells-Rodas",
"Yixin Wang",
"Pedro A. M. Mediano",
"Yingzhen Li"
] |
2024-06-25T16:38:27Z
|
2024-06-25T16:38:27Z
|
2311.03547
|
InterVLS: Interactive Model Understanding and Improvement with
Vision-Language Surrogates
|
Deep learning models are widely used in critical applications, highlighting the need for pre-deployment model understanding and improvement. Visual concept-based methods, while increasingly used for this purpose, face challenges: (1) most concepts lack interpretability, (2) existing methods require model knowledge, often unavailable at run time. Additionally, (3) there lacks a no-code method for post-understanding model improvement. Addressing these, we present InterVLS. The system facilitates model understanding by discovering text-aligned concepts, measuring their influence with model-agnostic linear surrogates. Employing visual analytics, InterVLS offers concept-based explanations and performance insights. It enables users to adjust concept influences to update a model, facilitating no-code model improvement. We evaluate InterVLS in a user study, illustrating its functionality with two scenarios. Results indicates that InterVLS is effective to help users identify influential concepts to a model, gain insights and adjust concept influence to improve the model. We conclude with a discussion based on our study results.
|
http://arxiv.org/pdf/2311.03547v2
|
[
"Jinbin Huang",
"Wenbin He",
"Liang Gou",
"Liu Ren",
"Chris Bryan"
] |
2024-06-25T16:37:48Z
|
2023-11-06T21:30:59Z
|
2406.17697
|
HGTDP-DTA: Hybrid Graph-Transformer with Dynamic Prompt for Drug-Target
Binding Affinity Prediction
|
Drug target binding affinity (DTA) is a key criterion for drug screening. Existing experimental methods are time-consuming and rely on limited structural and domain information. While learning-based methods can model sequence and structural information, they struggle to integrate contextual data and often lack comprehensive modeling of drug-target interactions. In this study, we propose a novel DTA prediction method, termed HGTDP-DTA, which utilizes dynamic prompts within a hybrid Graph-Transformer framework. Our method generates context-specific prompts for each drug-target pair, enhancing the model's ability to capture unique interactions. The introduction of prompt tuning further optimizes the prediction process by filtering out irrelevant noise and emphasizing task-relevant information, dynamically adjusting the input features of the molecular graph. The proposed hybrid Graph-Transformer architecture combines structural information from Graph Convolutional Networks (GCNs) with sequence information captured by Transformers, facilitating the interaction between global and local information. Additionally, we adopted the multi-view feature fusion method to project molecular graph views and affinity subgraph views into a common feature space, effectively combining structural and contextual information. Experiments on two widely used public datasets, Davis and KIBA, show that HGTDP-DTA outperforms state-of-the-art DTA prediction methods in both prediction performance and generalization ability.
|
http://arxiv.org/pdf/2406.17697v1
|
[
"Xi Xiao",
"Wentao Wang",
"Jiacheng Xie",
"Lijing Zhu",
"Gaofei Chen",
"Zhengji Li",
"Tianyang Wang",
"Min Xu"
] |
2024-06-25T16:33:33Z
|
2024-06-25T16:33:33Z
|
2406.17692
|
From Distributional to Overton Pluralism: Investigating Large Language
Model Alignment
|
The alignment process changes several properties of a large language model's (LLM's) output distribution. We analyze two aspects of post-alignment distributional shift of LLM responses. First, we re-examine previously reported reductions in response diversity post-alignment. Our analysis suggests that an apparent drop in the diversity of responses is largely explained by quality control and information aggregation. Alignment suppresses irrelevant and unhelpful content while shifting the output distribution toward longer responses that cover information spanning several responses from the base LLM, essentially presenting diverse information in a single response. Finding little evidence that alignment suppresses useful information, it is natural to ask the opposite question: do aligned models surface information that cannot be recovered from base models? Our second investigation shows this is not the case and the behavior of aligned models is recoverable from base models without fine-tuning. A combination of in-context examples and lower-resolution semantic hints about response content can elicit responses from base LLMs that are as similar to alignment-tuned LLM responses as alignment-tuned LLM responses are to each other. Taken together, these results indicate that current alignment techniques capture but do not extend the useful subset of assistant-like base LLM behavior, providing further evidence for the Superficial Alignment Hypothesis. They also show that in-context alignment can go surprisingly far as a strategy for imitating aligned LLMs without fine-tuning. Our code and data is available at https://github.com/thomlake/investigating-alignment.
|
http://arxiv.org/pdf/2406.17692v1
|
[
"Thom Lake",
"Eunsol Choi",
"Greg Durrett"
] |
2024-06-25T16:32:33Z
|
2024-06-25T16:32:33Z
|
2310.03575
|
Analysis of learning a flow-based generative model from limited sample
complexity
|
We study the problem of training a flow-based generative model, parametrized by a two-layer autoencoder, to sample from a high-dimensional Gaussian mixture. We provide a sharp end-to-end analysis of the problem. First, we provide a tight closed-form characterization of the learnt velocity field, when parametrized by a shallow denoising auto-encoder trained on a finite number $n$ of samples from the target distribution. Building on this analysis, we provide a sharp description of the corresponding generative flow, which pushes the base Gaussian density forward to an approximation of the target density. In particular, we provide closed-form formulae for the distance between the mean of the generated mixture and the mean of the target mixture, which we show decays as $Theta_n(frac{1}{n})$. Finally, this rate is shown to be in fact Bayes-optimal.
|
http://arxiv.org/pdf/2310.03575v2
|
[
"Hugo Cui",
"Florent Krzakala",
"Eric Vanden-Eijnden",
"Lenka Zdeborová"
] |
2024-06-25T16:32:20Z
|
2023-10-05T14:53:40Z
|
2302.12803
|
PiPar: Pipeline Parallelism for Collaborative Machine Learning
|
Collaborative machine learning (CML) techniques, such as federated learning, have been proposed to train deep learning models across multiple mobile devices and a server. CML techniques are privacy-preserving as a local model that is trained on each device instead of the raw data from the device is shared with the server. However, CML training is inefficient due to low resource utilization. We identify idling resources on the server and devices due to sequential computation and communication as the principal cause of low resource utilization. A novel framework PiPar that leverages pipeline parallelism for CML techniques is developed to substantially improve resource utilization. A new training pipeline is designed to parallelize the computations on different hardware resources and communication on different bandwidth resources, thereby accelerating the training process in CML. A low overhead automated parameter selection method is proposed to optimize the pipeline, maximizing the utilization of available resources. The experimental results confirm the validity of the underlying approach of PiPar and highlight that when compared to federated learning: (i) the idle time of the server can be reduced by up to 64.1x, and (ii) the overall training time can be accelerated by up to 34.6x under varying network conditions for a collection of six small and large popular deep neural networks and four datasets without sacrificing accuracy. It is also experimentally demonstrated that PiPar achieves performance benefits when incorporating differential privacy methods and operating in environments with heterogeneous devices and changing bandwidths.
|
http://arxiv.org/pdf/2302.12803v2
|
[
"Zihan Zhang",
"Philip Rodgers",
"Peter Kilpatrick",
"Ivor Spence",
"Blesson Varghese"
] |
2024-06-25T16:17:27Z
|
2022-12-01T20:51:47Z
|
2304.05099
|
Feudal Graph Reinforcement Learning
|
Graph-based representations and message-passing modular policies constitute prominent approaches to tackling composable control problems in Reinforcement Learning (RL). However, as shown by recent graph deep learning literature, such local message-passing operators can create information bottlenecks and hinder global coordination. The issue becomes more serious in tasks requiring high-level planning. In this work, we propose a novel methodology, named Feudal Graph Reinforcement Learning (FGRL), that addresses such challenges by relying on hierarchical RL and a pyramidal message-passing architecture. In particular, FGRL defines a hierarchy of policies where high-level commands are propagated from the top of the hierarchy down through a layered graph structure. The bottom layers mimic the morphology of the physical system, while the upper layers correspond to higher-order sub-modules. The resulting agents are then characterized by a committee of policies where actions at a certain level set goals for the level below, thus implementing a hierarchical decision-making structure that can naturally implement task decomposition. We evaluate the proposed framework on a graph clustering problem and MuJoCo locomotion tasks; simulation results show that FGRL compares favorably against relevant baselines. Furthermore, an in-depth analysis of the command propagation mechanism provides evidence that the introduced message-passing scheme favors learning hierarchical decision-making policies.
|
http://arxiv.org/pdf/2304.05099v4
|
[
"Tommaso Marzi",
"Arshjot Khehra",
"Andrea Cini",
"Cesare Alippi"
] |
2024-06-25T16:16:49Z
|
2023-04-11T09:51:13Z
|
2406.17837
|
Transformer Normalisation Layers and the Independence of Semantic
Subspaces
|
Recent works have shown that transformers can solve contextual reasoning tasks by internally executing computational graphs called circuits. Circuits often use attention to logically match information from subspaces of the representation, e.g. using position-in-sequence to identify the previous token. In this work, we consider a semantic subspace to be any independent subspace of the latent representation that can fully determine an attention distribution. We show that Pre-Norm, the placement of normalisation layer used by state-of-the-art transformers, violates this ability unless the model learns a strict representation structure of orthogonal spheres. This is because it causes linear subspaces to interfere through their common normalisation factor. Theoretically, we analyse circuit stability by modelling this interference as random noise on the $L_2$-norms of the query/key/value vectors, predicting a phenomenon of circuit collapse when sparse-attention shifts to a different token. Empirically, we investigate the sensitivity of real-world models trained for mathematical addition, observing a 1% rate of circuit collapse when the norms are artificially perturbed by $lesssim$10%. We contrast Pre-Norm with QKV-Norm, which places normalisation after the attention head's linear operators. Theoretically this relaxes the representational constraints. Empirically we observe comparable in-distribution but worse out-of-distribution performance.
|
http://arxiv.org/pdf/2406.17837v1
|
[
"Stephen Menary",
"Samuel Kaski",
"Andre Freitas"
] |
2024-06-25T16:16:38Z
|
2024-06-25T16:16:38Z
|
2312.07434
|
Multi-Modal Conformal Prediction Regions with Simple Structures by
Optimizing Convex Shape Templates
|
Conformal prediction is a statistical tool for producing prediction regions for machine learning models that are valid with high probability. A key component of conformal prediction algorithms is a emph{non-conformity score function} that quantifies how different a model's prediction is from the unknown ground truth value. Essentially, these functions determine the shape and the size of the conformal prediction regions. While prior work has gone into creating score functions that produce multi-model prediction regions, such regions are generally too complex for use in downstream planning and control problems. We propose a method that optimizes parameterized emph{shape template functions} over calibration data, which results in non-conformity score functions that produce prediction regions with minimum volume. Our approach results in prediction regions that are emph{multi-modal}, so they can properly capture residuals of distributions that have multiple modes, and emph{practical}, so each region is convex and can be easily incorporated into downstream tasks, such as a motion planner using conformal prediction regions. Our method applies to general supervised learning tasks, while we illustrate its use in time-series prediction. We provide a toolbox and present illustrative case studies of F16 fighter jets and autonomous vehicles, showing an up to $68%$ reduction in prediction region area compared to a circular baseline region.
|
http://arxiv.org/pdf/2312.07434v2
|
[
"Renukanandan Tumu",
"Matthew Cleaveland",
"Rahul Mangharam",
"George J. Pappas",
"Lars Lindemann"
] |
2024-06-25T16:10:41Z
|
2023-12-12T17:00:13Z
|
2306.14094
|
Locally Differentially Private Distributed Online Learning with
Guaranteed Optimality
|
Distributed online learning is gaining increased traction due to its unique ability to process large-scale datasets and streaming data. To address the growing public awareness and concern on privacy protection, plenty of algorithms have been proposed to enable differential privacy in distributed online optimization and learning. However, these algorithms often face the dilemma of trading learning accuracy for privacy. By exploiting the unique characteristics of online learning, this paper proposes an approach that tackles the dilemma and ensures both differential privacy and learning accuracy in distributed online learning. More specifically, while ensuring a diminishing expected instantaneous regret, the approach can simultaneously ensure a finite cumulative privacy budget, even in the infinite time horizon. To cater for the fully distributed setting, we adopt the local differential-privacy framework, which avoids the reliance on a trusted data curator, and, hence, provides stronger protection than the classic "centralized" (global) differential privacy. To the best of our knowledge, this is the first algorithm that successfully ensures both rigorous local differential privacy and learning accuracy. The effectiveness of the proposed algorithm is evaluated using machine learning tasks, including logistic regression on the the "mushrooms" datasets and CNN-based image classification on the "MNIST" and "CIFAR-10" datasets.
|
http://arxiv.org/pdf/2306.14094v2
|
[
"Ziqin Chen",
"Yongqiang Wang"
] |
2024-06-25T16:07:24Z
|
2023-06-25T02:05:34Z
|
2406.17673
|
LaTable: Towards Large Tabular Models
|
Tabular data is one of the most ubiquitous modalities, yet the literature on tabular generative foundation models is lagging far behind its text and vision counterparts. Creating such a model is hard, due to the heterogeneous feature spaces of different tabular datasets, tabular metadata (e.g. dataset description and feature headers), and tables lacking prior knowledge (e.g. feature order). In this work we propose LaTable: a novel tabular diffusion model that addresses these challenges and can be trained across different datasets. Through extensive experiments we find that LaTable outperforms baselines on in-distribution generation, and that finetuning LaTable can generate out-of-distribution datasets better with fewer samples. On the other hand, we explore the poor zero-shot performance of LaTable, and what it may teach us about building generative tabular foundation models with better zero- and few-shot generation capabilities.
|
http://arxiv.org/pdf/2406.17673v1
|
[
"Boris van Breugel",
"Jonathan Crabbé",
"Rob Davis",
"Mihaela van der Schaar"
] |
2024-06-25T16:03:50Z
|
2024-06-25T16:03:50Z
|
2403.16883
|
GLAD: Improving Latent Graph Generative Modeling with Simple
Quantization
|
Exploring the graph latent structures has not garnered much attention in the graph generative research field. Yet, exploiting the latent space is as crucial as working on the data space for discrete data such as graphs. However, previous methods either failed to preserve the permutation symmetry of graphs or lacked an effective approaches to model appropriately within the latent space. To mitigate those issues, we propose a simple, yet effective discrete latent graph diffusion generative model. Our model, namely GLAD, not only overcomes the drawbacks of existing latent approaches, but also alleviates inherent issues present in diffusion methods applied on the graph space. We validate our generative model on the molecular benchmark datasets, on which it demonstrates competitive performance compared with the state-of-the-art baselines.
|
http://arxiv.org/pdf/2403.16883v2
|
[
"Van Khoa Nguyen",
"Yoann Boget",
"Frantzeska Lavda",
"Alexandros Kalousis"
] |
2024-06-25T16:01:57Z
|
2024-03-25T15:53:32Z
|
2406.17660
|
Grass: Compute Efficient Low-Memory LLM Training with Structured Sparse
Gradients
|
Large language model (LLM) training and finetuning are often bottlenecked by limited GPU memory. While existing projection-based optimization methods address this by projecting gradients into a lower-dimensional subspace to reduce optimizer state memory, they typically rely on dense projection matrices, which can introduce computational and memory overheads. In this work, we propose Grass (GRAdient Stuctured Sparsification), a novel approach that leverages sparse projections to transform gradients into structured sparse updates. This design not only significantly reduces memory usage for optimizer states but also minimizes gradient memory footprint, computation, and communication costs, leading to substantial throughput improvements. Extensive experiments on pretraining and finetuning tasks demonstrate that Grass achieves competitive performance to full-rank training and existing projection-based methods. Notably, Grass enables half-precision pretraining of a 13B parameter LLaMA model on a single 40GB A100 GPU--a feat infeasible for previous methods--and yields up to a $2times$ throughput improvement on an 8-GPU system. Code can be found at https://github.com/aashiqmuhamed/GRASS .
|
http://arxiv.org/pdf/2406.17660v1
|
[
"Aashiq Muhamed",
"Oscar Li",
"David Woodruff",
"Mona Diab",
"Virginia Smith"
] |
2024-06-25T15:50:32Z
|
2024-06-25T15:50:32Z
|
2406.17649
|
Privacy Preserving Reinforcement Learning for Population Processes
|
We consider the problem of privacy protection in Reinforcement Learning (RL) algorithms that operate over population processes, a practical but understudied setting that includes, for example, the control of epidemics in large populations of dynamically interacting individuals. In this setting, the RL algorithm interacts with the population over $T$ time steps by receiving population-level statistics as state and performing actions which can affect the entire population at each time step. An individual's data can be collected across multiple interactions and their privacy must be protected at all times. We clarify the Bayesian semantics of Differential Privacy (DP) in the presence of correlated data in population processes through a Pufferfish Privacy analysis. We then give a meta algorithm that can take any RL algorithm as input and make it differentially private. This is achieved by taking an approach that uses DP mechanisms to privatize the state and reward signal at each time step before the RL algorithm receives them as input. Our main theoretical result shows that the value-function approximation error when applying standard RL algorithms directly to the privatized states shrinks quickly as the population size and privacy budget increase. This highlights that reasonable privacy-utility trade-offs are possible for differentially private RL algorithms in population processes. Our theoretical findings are validated by experiments performed on a simulated epidemic control problem over large population sizes.
|
http://arxiv.org/pdf/2406.17649v1
|
[
"Samuel Yang-Zhao",
"Kee Siong Ng"
] |
2024-06-25T15:41:26Z
|
2024-06-25T15:41:26Z
|
2406.17835
|
The Use of AI-Robotic Systems for Scientific Discovery
|
The process of developing theories and models and testing them with experiments is fundamental to the scientific method. Automating the entire scientific method then requires not only automation of the induction of theories from data, but also experimentation from design to implementation. This is the idea behind a robot scientist -- a coupled system of AI and laboratory robotics that has agency to test hypotheses with real-world experiments. In this chapter we explore some of the fundamentals of robot scientists in the philosophy of science. We also map the activities of a robot scientist to machine learning paradigms, and argue that the scientific method shares an analogy with active learning. We demonstrate these concepts using examples from previous robot scientists, and also from Genesis: a next generation robot scientist designed for research in systems biology, comprising a micro-fluidic system with 1000 computer-controlled micro-bioreactors and interpretable models based in controlled vocabularies and logic.
|
http://arxiv.org/pdf/2406.17835v1
|
[
"Alexander H. Gower",
"Konstantin Korovin",
"Daniel Brunnsåker",
"Filip Kronström",
"Gabriel K. Reder",
"Ievgeniia A. Tiukova",
"Ronald S. Reiserer",
"John P. Wikswo",
"Ross D. King"
] |
2024-06-25T15:33:01Z
|
2024-06-25T15:33:01Z
|
2406.17640
|
BayTTA: Uncertainty-aware medical image classification with optimized
test-time augmentation using Bayesian model averaging
|
Test-time augmentation (TTA) is a well-known technique employed during the testing phase of computer vision tasks. It involves aggregating multiple augmented versions of input data. Combining predictions using a simple average formulation is a common and straightforward approach after performing TTA. This paper introduces a novel framework for optimizing TTA, called BayTTA (Bayesian-based TTA), which is based on Bayesian Model Averaging (BMA). First, we generate a model list associated with different variations of the input data created through TTA. Then, we use BMA to combine model predictions weighted by their respective posterior probabilities. Such an approach allows one to take into account model uncertainty, and thus to enhance the predictive performance of the related machine learning or deep learning model. We evaluate the performance of BayTTA on various public data, including three medical image datasets comprising skin cancer, breast cancer, and chest X-ray images and two well-known gene editing datasets, CRISPOR and GUIDE-seq. Our experimental results indicate that BayTTA can be effectively integrated into state-of-the-art deep learning models used in medical image analysis as well as into some popular pre-trained CNN models such as VGG-16, MobileNetV2, DenseNet201, ResNet152V2, and InceptionRes-NetV2, leading to the enhancement in their accuracy and robustness performance.
|
http://arxiv.org/pdf/2406.17640v1
|
[
"Zeinab Sherkatghanad",
"Moloud Abdar",
"Mohammadreza Bakhtyari",
"Vladimir Makarenkov"
] |
2024-06-25T15:24:06Z
|
2024-06-25T15:24:06Z
|
2406.17633
|
Knowledge Distillation in Automated Annotation: Supervised Text
Classification with LLM-Generated Training Labels
|
Computational social science (CSS) practitioners often rely on human-labeled data to fine-tune supervised text classifiers. We assess the potential for researchers to augment or replace human-generated training data with surrogate training labels from generative large language models (LLMs). We introduce a recommended workflow and test this LLM application by replicating 14 classification tasks and measuring performance. We employ a novel corpus of English-language text classification data sets from recent CSS articles in high-impact journals. Because these data sets are stored in password-protected archives, our analyses are less prone to issues of contamination. For each task, we compare supervised classifiers fine-tuned using GPT-4 labels against classifiers fine-tuned with human annotations and against labels from GPT-4 and Mistral-7B with few-shot in-context learning. Our findings indicate that supervised classification models fine-tuned on LLM-generated labels perform comparably to models fine-tuned with labels from human annotators. Fine-tuning models using LLM-generated labels can be a fast, efficient and cost-effective method of building supervised text classifiers.
|
http://arxiv.org/pdf/2406.17633v1
|
[
"Nicholas Pangakis",
"Samuel Wolken"
] |
2024-06-25T15:20:25Z
|
2024-06-25T15:20:25Z
|
2406.17630
|
KANQAS: Kolmogorov Arnold Network for Quantum Architecture Search
|
Quantum architecture search~(QAS) is a promising direction for optimization and automated design of quantum circuits towards quantum advantage. Recent techniques in QAS focus on machine learning-based approaches from reinforcement learning, like deep Q-network. While multi-layer perceptron-based deep Q-networks have been applied for QAS, their interpretability remains challenging due to the high number of parameters. In this work, we evaluate the practicality of KANs in quantum architecture search problems, analyzing their efficiency in terms of the probability of success, frequency of optimal solutions and their dependencies on various degrees of freedom of the network. In a noiseless scenario, the probability of success and the number of optimal quantum circuit configurations to generate the multi-qubit maximally entangled states are significantly higher than MLPs. Moreover in noisy scenarios, KAN can achieve a better fidelity in approximating maximally entangled state than MLPs, where the performance of the MLP significantly depends on the choice of activation function. Further investigation reveals that KAN requires a very small number of learnable parameters compared to MLPs, however, the average time of executing each episode for KAN is much higher.
|
http://arxiv.org/pdf/2406.17630v1
|
[
"Akash Kundu",
"Aritra Sarkar",
"Abhishek Sadhu"
] |
2024-06-25T15:17:01Z
|
2024-06-25T15:17:01Z
|
2211.05408
|
Controlling Moments with Kernel Stein Discrepancies
|
Kernel Stein discrepancies (KSDs) measure the quality of a distributional approximation and can be computed even when the target density has an intractable normalizing constant. Notable applications include the diagnosis of approximate MCMC samplers and goodness-of-fit tests for unnormalized statistical models. The present work analyzes the convergence control properties of KSDs. We first show that standard KSDs used for weak convergence control fail to control moment convergence. To address this limitation, we next provide sufficient conditions under which alternative diffusion KSDs control both moment and weak convergence. As an immediate consequence we develop, for each $q > 0$, the first KSDs known to exactly characterize $q$-Wasserstein convergence.
|
http://arxiv.org/pdf/2211.05408v4
|
[
"Heishiro Kanagawa",
"Alessandro Barp",
"Arthur Gretton",
"Lester Mackey"
] |
2024-06-25T15:16:17Z
|
2022-11-10T08:24:52Z
|
2406.17627
|
Querying Labeled Time Series Data with Scenario Programs
|
In order to ensure autonomous vehicles are safe for on-road deployment, simulation-based testing has become an integral complement to on-road testing. The rise in simulation testing and validation reflects a growing need to verify that AV behavior is consistent with desired outcomes even in edge case scenarios $-$ which may seldom or never appear in on-road testing data. This raises a critical question: to what extent are AV failures in simulation consistent with data collected from real-world testing? As a result of the gap between simulated and real sensor data (sim-to-real gap), failures in simulation can either be spurious (simulation- or simulator-specific issues) or relevant (safety-critical AV system issues). One possible method for validating if simulated time series failures are consistent with real world time series sensor data could involve retrieving instances of the failure scenario from a real-world time series dataset, in order to understand AV performance in these scenarios. Adopting this strategy, we propose a formal definition of what constitutes a match between a real-world labeled time series data item and a simulated scenario written from a fragment of the Scenic probabilistic programming language for simulation generation. With this definition of a match, we develop a querying algorithm that identifies the subset of a labeled time series dataset matching a given scenario. To allow this approach to be used to verify the safety of other cyber-physical systems (CPS), we present a definition and algorithm for matching scalable beyond the autonomous vehicles domain. Experiments demonstrate the precision and scalability of the algorithm for a set of challenging and uncommon time series scenarios identified from the nuScenes autonomous driving dataset. We include a full system implementation of the querying algorithm freely available for use across a wide range of CPS.
|
http://arxiv.org/pdf/2406.17627v1
|
[
"Devan Shanker"
] |
2024-06-25T15:15:27Z
|
2024-06-25T15:15:27Z
|
2403.15022
|
Insights into the Lottery Ticket Hypothesis and Iterative Magnitude
Pruning
|
Lottery ticket hypothesis for deep neural networks emphasizes the importance of initialization used to re-train the sparser networks obtained using the iterative magnitude pruning process. An explanation for why the specific initialization proposed by the lottery ticket hypothesis tends to work better in terms of generalization (and training) performance has been lacking. Moreover, the underlying principles in iterative magnitude pruning, like the pruning of smaller magnitude weights and the role of the iterative process, lack full understanding and explanation. In this work, we attempt to provide insights into these phenomena by empirically studying the volume/geometry and loss landscape characteristics of the solutions obtained at various stages of the iterative magnitude pruning process.
|
http://arxiv.org/pdf/2403.15022v3
|
[
"Tausifa Jan Saleem",
"Ramanjit Ahuja",
"Surendra Prasad",
"Brejesh Lall"
] |
2024-06-25T15:14:12Z
|
2024-03-22T08:11:14Z
|
2406.17834
|
Univariate Skeleton Prediction in Multivariate Systems Using
Transformers
|
Symbolic regression (SR) methods attempt to learn mathematical expressions that approximate the behavior of an observed system. However, when dealing with multivariate systems, they often fail to identify the functional form that explains the relationship between each variable and the system's response. To begin to address this, we propose an explainable neural SR method that generates univariate symbolic skeletons that aim to explain how each variable influences the system's response. By analyzing multiple sets of data generated artificially, where one input variable varies while others are fixed, relationships are modeled separately for each input variable. The response of such artificial data sets is estimated using a regression neural network (NN). Finally, the multiple sets of input-response pairs are processed by a pre-trained Multi-Set Transformer that solves a problem we termed Multi-Set Skeleton Prediction and outputs a univariate symbolic skeleton. Thus, such skeletons represent explanations of the function approximated by the regression NN. Experimental results demonstrate that this method learns skeleton expressions matching the underlying functions and outperforms two GP-based and two neural SR methods.
|
http://arxiv.org/pdf/2406.17834v1
|
[
"Giorgio Morales",
"John W. Sheppard"
] |
2024-06-25T15:07:06Z
|
2024-06-25T15:07:06Z
|
2302.12355
|
Fundamental Bounds on Online Strategic Classification
|
We study the problem of online binary classification where strategic agents can manipulate their observable features in predefined ways, modeled by a manipulation graph, in order to receive a positive classification. We show this setting differs in fundamental ways from non-strategic online classification. For instance, whereas in the non-strategic case, a mistake bound of $ln|H|$ is achievable via the halving algorithm when the target function belongs to a known class $H$, we show that no deterministic algorithm can achieve a mistake bound $o(Delta)$ in the strategic setting, where $Delta$ is the maximum degree of the manipulation graph (even when $|H|=O(Delta)$). We obtain an algorithm achieving mistake bound $O(Deltaln|H|)$. We also extend this to the agnostic setting and obtain an algorithm with a $Delta$ multiplicative regret, and we show no deterministic algorithm can achieve $o(Delta)$ multiplicative regret. Next, we study two randomized models based on whether the random choices are made before or after agents respond, and show they exhibit fundamental differences. In the first model, at each round the learner deterministically chooses a probability distribution over classifiers inducing expected values on each vertex (probabilities of being classified as positive), which the strategic agents respond to. We show that any learner in this model has to suffer linear regret. On the other hand, in the second model, while the adversary who selects the next agent must respond to the learner's probability distribution over classifiers, the agent then responds to the actual hypothesis classifier drawn from this distribution. Surprisingly, we show this model is more advantageous to the learner, and we design randomized algorithms that achieve sublinear regret bounds against both oblivious and adaptive adversaries.
|
http://arxiv.org/pdf/2302.12355v2
|
[
"Saba Ahmadi",
"Avrim Blum",
"Kunhe Yang"
] |
2024-06-25T15:06:33Z
|
2023-02-23T22:39:43Z
|
2406.17615
|
Aligning Programming Language and Natural Language: Exploring Design
Choices in Multi-Modal Transformer-Based Embedding for Bug Localization
|
Bug localization refers to the identification of source code files which is in a programming language and also responsible for the unexpected behavior of software using the bug report, which is a natural language. As bug localization is labor-intensive, bug localization models are employed to assist software developers. Due to the domain difference between source code files and bug reports, modern bug-localization systems, based on deep learning models, rely heavily on embedding techniques that project bug reports and source code files into a shared vector space. The creation of an embedding involves several design choices, but the impact of these choices on the quality of embedding and the performance of bug localization models remains unexplained in current research. To address this gap, our study evaluated 14 distinct embedding models to gain insights into the effects of various design choices. Subsequently, we developed bug localization models utilizing these embedding models to assess the influence of these choices on the performance of the localization models. Our findings indicate that the pre-training strategies significantly affect the quality of the embedding. Moreover, we discovered that the familiarity of the embedding models with the data has a notable impact on the bug localization model's performance. Notably, when the training and testing data are collected from different projects, the performance of the bug localization models exhibits substantial fluctuations.
|
http://arxiv.org/abs/2406.17615v1
|
[
"Partha Chakraborty",
"Venkatraman Arumugam",
"Meiyappan Nagappan"
] |
2024-06-25T15:01:39Z
|
2024-06-25T15:01:39Z
|
2406.17611
|
Distributed Training of Large Graph Neural Networks with Variable
Communication Rates
|
Training Graph Neural Networks (GNNs) on large graphs presents unique challenges due to the large memory and computing requirements. Distributed GNN training, where the graph is partitioned across multiple machines, is a common approach to training GNNs on large graphs. However, as the graph cannot generally be decomposed into small non-interacting components, data communication between the training machines quickly limits training speeds. Compressing the communicated node activations by a fixed amount improves the training speeds, but lowers the accuracy of the trained GNN. In this paper, we introduce a variable compression scheme for reducing the communication volume in distributed GNN training without compromising the accuracy of the learned model. Based on our theoretical analysis, we derive a variable compression method that converges to a solution equivalent to the full communication case, for all graph partitioning schemes. Our empirical results show that our method attains a comparable performance to the one obtained with full communication. We outperform full communication at any fixed compression ratio for any communication budget.
|
http://arxiv.org/pdf/2406.17611v1
|
[
"Juan Cervino",
"Md Asadullah Turja",
"Hesham Mostafa",
"Nageen Himayat",
"Alejandro Ribeiro"
] |
2024-06-25T14:57:38Z
|
2024-06-25T14:57:38Z
|
2406.17606
|
Diffusion-based Adversarial Purification for Intrusion Detection
|
The escalating sophistication of cyberattacks has encouraged the integration of machine learning techniques in intrusion detection systems, but the rise of adversarial examples presents a significant challenge. These crafted perturbations mislead ML models, enabling attackers to evade detection or trigger false alerts. As a reaction, adversarial purification has emerged as a compelling solution, particularly with diffusion models showing promising results. However, their purification potential remains unexplored in the context of intrusion detection. This paper demonstrates the effectiveness of diffusion models in purifying adversarial examples in network intrusion detection. Through a comprehensive analysis of the diffusion parameters, we identify optimal configurations maximizing adversarial robustness with minimal impact on normal performance. Importantly, this study reveals insights into the relationship between diffusion noise and diffusion steps, representing a novel contribution to the field. Our experiments are carried out on two datasets and against 5 adversarial attacks. The implementation code is publicly available.
|
http://arxiv.org/pdf/2406.17606v1
|
[
"Mohamed Amine Merzouk",
"Erwan Beurier",
"Reda Yaich",
"Nora Boulahia-Cuppens",
"Frédéric Cuppens"
] |
2024-06-25T14:48:28Z
|
2024-06-25T14:48:28Z
|
2406.17597
|
Constructing structured tensor priors for Bayesian inverse problems
|
Specifying a prior distribution is an essential part of solving Bayesian inverse problems. The prior encodes a belief on the nature of the solution and this regularizes the problem. In this article we completely characterize a Gaussian prior that encodes the belief that the solution is a structured tensor. We first define the notion of (A,b)-constrained tensors and show that they describe a large variety of different structures such as Hankel, circulant, triangular, symmetric, and so on. Then we completely characterize the Gaussian probability distribution of such tensors by specifying its mean vector and covariance matrix. Furthermore, explicit expressions are proved for the covariance matrix of tensors whose entries are invariant under a permutation. These results unlock a whole new class of priors for Bayesian inverse problems. We illustrate how new kernel functions can be designed and efficiently computed and apply our results on two particular Bayesian inverse problems: completing a Hankel matrix from a few noisy measurements and learning an image classifier of handwritten digits. The effectiveness of the proposed priors is demonstrated for both problems. All applications have been implemented as reactive Pluto notebooks in Julia.
|
http://arxiv.org/pdf/2406.17597v1
|
[
"Kim Batselier"
] |
2024-06-25T14:40:34Z
|
2024-06-25T14:40:34Z
|
2303.09470
|
Learning with Noisy Labels through Learnable Weighting and Centroid
Similarity
|
We introduce a novel method for training machine learning models in the presence of noisy labels, which are prevalent in domains such as medical diagnosis and autonomous driving and have the potential to degrade a model's generalization performance. Inspired by established literature that highlights how deep learning models are prone to overfitting to noisy samples in the later epochs of training, we propose a strategic approach. This strategy leverages the distance to class centroids in the latent space and incorporates a discounting mechanism, aiming to diminish the influence of samples that lie distant from all class centroids. By doing so, we effectively counteract the adverse effects of noisy labels. The foundational premise of our approach is the assumption that samples situated further from their respective class centroid in the initial stages of training are more likely to be associated with noise. Our methodology is grounded in robust theoretical principles and has been validated empirically through extensive experiments on several benchmark datasets. Our results show that our method consistently outperforms the existing state-of-the-art techniques, achieving significant improvements in classification accuracy in the presence of noisy labels. The code for our proposed loss function and supplementary materials is available at https://github.com/wanifarooq/NCOD
|
http://arxiv.org/pdf/2303.09470v2
|
[
"Farooq Ahmad Wani",
"Maria Sofia Bucarelli",
"Fabrizio Silvestri"
] |
2024-06-25T14:36:33Z
|
2023-03-16T16:43:24Z
|
2406.17585
|
Learning Dynamic Bayesian Networks from Data: Foundations, First
Principles and Numerical Comparisons
|
In this paper, we present a guide to the foundations of learning Dynamic Bayesian Networks (DBNs) from data in the form of multiple samples of trajectories for some length of time. We present the formalism for a generic as well as a set of common types of DBNs for particular variable distributions. We present the analytical form of the models, with a comprehensive discussion on the interdependence between structure and weights in a DBN model and their implications for learning. Next, we give a broad overview of learning methods and describe and categorize them based on the most important statistical features, and how they treat the interplay between learning structure and weights. We give the analytical form of the likelihood and Bayesian score functions, emphasizing the distinction from the static case. We discuss functions used in optimization to enforce structural requirements. We briefly discuss more complex extensions and representations. Finally we present a set of comparisons in different settings for various distinct but representative algorithms across the variants.
|
http://arxiv.org/pdf/2406.17585v1
|
[
"Vyacheslav Kungurtsev",
"Petr Rysavy",
"Fadwa Idlahcen",
"Pavel Rytir",
"Ales Wodecki"
] |
2024-06-25T14:28:17Z
|
2024-06-25T14:28:17Z
|
2407.01467
|
The Balanced-Pairwise-Affinities Feature Transform
|
The Balanced-Pairwise-Affinities (BPA) feature transform is designed to upgrade the features of a set of input items to facilitate downstream matching or grouping related tasks. The transformed set encodes a rich representation of high order relations between the input features. A particular min-cost-max-flow fractional matching problem, whose entropy regularized version can be approximated by an optimal transport (OT) optimization, leads to a transform which is efficient, differentiable, equivariant, parameterless and probabilistically interpretable. While the Sinkhorn OT solver has been adapted extensively in many contexts, we use it differently by minimizing the cost between a set of features to $itself$ and using the transport plan's $rows$ as the new representation. Empirically, the transform is highly effective and flexible in its use and consistently improves networks it is inserted into, in a variety of tasks and training schemes. We demonstrate state-of-the-art results in few-shot classification, unsupervised image clustering and person re-identification. Code is available at url{github.com/DanielShalam/BPA}.
|
http://arxiv.org/pdf/2407.01467v1
|
[
"Daniel Shalam",
"Simon Korman"
] |
2024-06-25T14:28:05Z
|
2024-06-25T14:28:05Z
|
2406.17583
|
Towards Compositional Interpretability for XAI
|
Artificial intelligence (AI) is currently based largely on black-box machine learning models which lack interpretability. The field of eXplainable AI (XAI) strives to address this major concern, being critical in high-stakes areas such as the finance, legal and health sectors. We present an approach to defining AI models and their interpretability based on category theory. For this we employ the notion of a compositional model, which sees a model in terms of formal string diagrams which capture its abstract structure together with its concrete implementation. This comprehensive view incorporates deterministic, probabilistic and quantum models. We compare a wide range of AI models as compositional models, including linear and rule-based models, (recurrent) neural networks, transformers, VAEs, and causal and DisCoCirc models. Next we give a definition of interpretation of a model in terms of its compositional structure, demonstrating how to analyse the interpretability of a model, and using this to clarify common themes in XAI. We find that what makes the standard 'intrinsically interpretable' models so transparent is brought out most clearly diagrammatically. This leads us to the more general notion of compositionally-interpretable (CI) models, which additionally include, for instance, causal, conceptual space, and DisCoCirc models. We next demonstrate the explainability benefits of CI models. Firstly, their compositional structure may allow the computation of other quantities of interest, and may facilitate inference from the model to the modelled phenomenon by matching its structure. Secondly, they allow for diagrammatic explanations for their behaviour, based on influence constraints, diagram surgery and rewrite explanations. Finally, we discuss many future directions for the approach, raising the question of how to learn such meaningfully structured models in practice.
|
http://arxiv.org/pdf/2406.17583v1
|
[
"Sean Tull",
"Robin Lorenz",
"Stephen Clark",
"Ilyas Khan",
"Bob Coecke"
] |
2024-06-25T14:27:03Z
|
2024-06-25T14:27:03Z
|
2406.17576
|
Leveraging Reinforcement Learning in Red Teaming for Advanced Ransomware
Attack Simulations
|
Ransomware presents a significant and increasing threat to individuals and organizations by encrypting their systems and not releasing them until a large fee has been extracted. To bolster preparedness against potential attacks, organizations commonly conduct red teaming exercises, which involve simulated attacks to assess existing security measures. This paper proposes a novel approach utilizing reinforcement learning (RL) to simulate ransomware attacks. By training an RL agent in a simulated environment mirroring real-world networks, effective attack strategies can be learned quickly, significantly streamlining traditional, manual penetration testing processes. The attack pathways revealed by the RL agent can provide valuable insights to the defense team, helping them identify network weak points and develop more resilient defensive measures. Experimental results on a 152-host example network confirm the effectiveness of the proposed approach, demonstrating the RL agent's capability to discover and orchestrate attacks on high-value targets while evading honeyfiles (decoy files strategically placed to detect unauthorized access).
|
http://arxiv.org/pdf/2406.17576v1
|
[
"Cheng Wang",
"Christopher Redino",
"Ryan Clark",
"Abdul Rahman",
"Sal Aguinaga",
"Sathvik Murli",
"Dhruv Nandakumar",
"Roland Rao",
"Lanxiao Huang",
"Daniel Radke",
"Edward Bowen"
] |
2024-06-25T14:16:40Z
|
2024-06-25T14:16:40Z
|
2406.17830
|
Treatment of Statistical Estimation Problems in Randomized Smoothing for
Adversarial Robustness
|
Randomized smoothing is a popular certified defense against adversarial attacks. In its essence, we need to solve a problem of statistical estimation which is usually very time-consuming since we need to perform numerous (usually $10^5$) forward passes of the classifier for every point to be certified. In this paper, we review the statistical estimation problems for randomized smoothing to find out if the computational burden is necessary. In particular, we consider the (standard) task of adversarial robustness where we need to decide if a point is robust at a certain radius or not using as few samples as possible while maintaining statistical guarantees. We present estimation procedures employing confidence sequences enjoying the same statistical guarantees as the standard methods, with the optimal sample complexities for the estimation task and empirically demonstrate their good performance. Additionally, we provide a randomized version of Clopper-Pearson confidence intervals resulting in strictly stronger certificates.
|
http://arxiv.org/pdf/2406.17830v1
|
[
"Vaclav Voracek"
] |
2024-06-25T14:00:55Z
|
2024-06-25T14:00:55Z
|
2406.17563
|
Multi-property Steering of Large Language Models with Dynamic Activation
Composition
|
Activation steering methods were shown to be effective in conditioning language model generation by additively intervening over models' intermediate representations. However, the evaluation of these techniques has so far been limited to single conditioning properties and synthetic settings. In this work, we conduct a comprehensive evaluation of various activation steering strategies, highlighting the property-dependent nature of optimal parameters to ensure a robust effect throughout generation. To address this issue, we propose Dynamic Activation Composition, an information-theoretic approach to modulate the steering intensity of one or more properties throughout generation. Our experiments on multi-property steering show that our method successfully maintains high conditioning while minimizing the impact of conditioning on generation fluency.
|
http://arxiv.org/pdf/2406.17563v1
|
[
"Daniel Scalena",
"Gabriele Sarti",
"Malvina Nissim"
] |
2024-06-25T14:00:42Z
|
2024-06-25T14:00:42Z
|
2311.15317
|
Generalized Graph Prompt: Toward a Unification of Pre-Training and
Downstream Tasks on Graphs
|
Graph neural networks have emerged as a powerful tool for graph representation learning, but their performance heavily relies on abundant task-specific supervision. To reduce labeling requirement, the "pre-train, prompt" paradigms have become increasingly common. However, existing study of prompting on graphs is limited, lacking a universal treatment to appeal to different downstream tasks. In this paper, we propose GraphPrompt, a novel pre-training and prompting framework on graphs. GraphPrompt not only unifies pre-training and downstream tasks into a common task template but also employs a learnable prompt to assist a downstream task in locating the most relevant knowledge from the pre-trained model in a task-specific manner. To further enhance GraphPrompt in these two stages, we extend it into GraphPrompt+ with two major enhancements. First, we generalize several popular graph pre-training tasks beyond simple link prediction to broaden the compatibility with our task template. Second, we propose a more generalized prompt design that incorporates a series of prompt vectors within every layer of the pre-trained graph encoder, in order to capitalize on the hierarchical information across different layers beyond just the readout layer. Finally, we conduct extensive experiments on five public datasets to evaluate and analyze GraphPrompt and GraphPrompt+.
|
http://arxiv.org/pdf/2311.15317v3
|
[
"Xingtong Yu",
"Zhenghao Liu",
"Yuan Fang",
"Zemin Liu",
"Sihong Chen",
"Xinming Zhang"
] |
2024-06-25T13:53:57Z
|
2023-11-26T14:35:28Z
|
2406.17828
|
Extreme Learning Machines for Fast Training of Click-Through Rate
Prediction Models
|
Extreme Learning Machines (ELM) provide a fast alternative to traditional gradient-based learning in neural networks, offering rapid training and robust generalization capabilities. Its theoretical basis shows its universal approximation capability. We explore the application of ELMs for the task of Click-Through Rate (CTR) prediction, which is largely unexplored by ELMs due to the high dimensionality of the problem. We introduce an ELM-based model enhanced with embedding layers to improve the performance on CTR tasks, which is a novel addition to the field. Experimental results on benchmark datasets, including Avazu and Criteo, demonstrate that our proposed ELM with embeddings achieves competitive F1 results while significantly reducing training time compared to state-of-the-art models such as Masknet. Our findings show that ELMs can be useful for CTR prediction, especially when fast training is needed.
|
http://arxiv.org/pdf/2406.17828v1
|
[
"Ergun Biçici"
] |
2024-06-25T13:50:00Z
|
2024-06-25T13:50:00Z
|
2406.17556
|
Modularity Based Community Detection in Hypergraphs
|
In this paper, we propose a scalable community detection algorithm using hypergraph modularity function, h-Louvain. It is an adaptation of the classical Louvain algorithm in the context of hypergraphs. We observe that a direct application of the Louvain algorithm to optimize the hypergraph modularity function often fails to find meaningful communities. We propose a solution to this issue by adjusting the initial stage of the algorithm via carefully and dynamically tuned linear combination of the graph modularity function of the corresponding two-section graph and the desired hypergraph modularity function. The process is guided by Bayesian optimization of the hyper-parameters of the proposed procedure. Various experiments on synthetic as well as real-world networks are performed showing that this process yields improved results in various regimes.
|
http://arxiv.org/pdf/2406.17556v1
|
[
"Bogumił Kamiński",
"Paweł Misiorek",
"Paweł Prałat",
"François Théberge"
] |
2024-06-25T13:49:56Z
|
2024-06-25T13:49:56Z
|
2401.02721
|
A Cost-Efficient FPGA Implementation of Tiny Transformer Model using
Neural ODE
|
Transformer has been adopted to a wide range of tasks and shown to outperform CNNs and RNNs while it suffers from high training cost and computational complexity. To address these issues, a hybrid approach has become a recent research trend, which replaces a part of ResNet with an MHSA (Multi-Head Self-Attention). In this paper, we propose a lightweight hybrid model which uses Neural ODE (Ordinary Differential Equation) as a backbone instead of ResNet for 12.1$times$ parameter reduction. For the STL10 dataset, the proposed model achieves 80.15% top-1 accuracy which is comparable to ResNet50. Then, the proposed model is deployed on a modest-sized FPGA device for edge computing. To further reduce FPGA resource utilization, the model is quantized following QAT (Quantization Aware Training) scheme instead of PTQ (Post Training Quantization) to suppress the accuracy loss. As a result, an extremely lightweight Transformer-based model can be implemented on resource-limited FPGAs. The weights of the feature extraction network are stored on-chip to minimize the memory transfer overhead, allowing faster inference. By eliminating the overhead of memory transfers, inference can be executed seamlessly, leading to accelerated inference. The proposed FPGA implementation achieves a 34.01$times$ speedup for the backbone and MHSA parts, and it achieves an overall 9.85$times$ speedup when taking into account software pre- and post-processing. It also achieves an overall 7.10$times$ higher energy efficiency compared to the ARM Cortex-A53 CPU.
|
http://arxiv.org/pdf/2401.02721v2
|
[
"Ikumi Okubo",
"Keisuke Sugiura",
"Hiroki Matsutani"
] |
2024-06-25T13:49:31Z
|
2024-01-05T09:32:39Z
|
2406.14425
|
SynDARin: Synthesising Datasets for Automated Reasoning in Low-Resource
Languages
|
Question Answering (QA) datasets have been instrumental in developing and evaluating Large Language Model (LLM) capabilities. However, such datasets are scarce for languages other than English due to the cost and difficulties of collection and manual annotation. This means that producing novel models and measuring the performance of multilingual LLMs in low-resource languages is challenging. To mitigate this, we propose $textbf{S}$yn$textbf{DAR}$in, a method for generating and validating QA datasets for low-resource languages. We utilize parallel content mining to obtain $textit{human-curated}$ paragraphs between English and the target language. We use the English data as context to $textit{generate}$ synthetic multiple-choice (MC) question-answer pairs, which are automatically translated and further validated for quality. Combining these with their designated non-English $textit{human-curated}$ paragraphs form the final QA dataset. The method allows to maintain the content quality, reduces the likelihood of factual errors, and circumvents the need for costly annotation. To test the method, we created a QA dataset with $1.2$K samples for the Armenian language. The human evaluation shows that $98%$ of the generated English data maintains quality and diversity in the question types and topics, while the translation validation pipeline can filter out $sim70%$ of data with poor quality. We use the dataset to benchmark state-of-the-art LLMs, showing their inability to achieve human accuracy with some model performances closer to random chance. This shows that the generated dataset is non-trivial and can be used to evaluate reasoning capabilities in low-resource language.
|
http://arxiv.org/pdf/2406.14425v2
|
[
"Gayane Ghazaryan",
"Erik Arakelyan",
"Pasquale Minervini",
"Isabelle Augenstein"
] |
2024-06-25T13:48:41Z
|
2024-06-20T15:49:28Z
|
2406.09335
|
Instance-level quantitative saliency in multiple sclerosis lesion
segmentation
|
In recent years, explainable methods for artificial intelligence (XAI) have tried to reveal and describe models' decision mechanisms in the case of classification tasks. However, XAI for semantic segmentation and in particular for single instances has been little studied to date. Understanding the process underlying automatic segmentation of single instances is crucial to reveal what information was used to detect and segment a given object of interest. In this study, we proposed two instance-level explanation maps for semantic segmentation based on SmoothGrad and Grad-CAM++ methods. Then, we investigated their relevance for the detection and segmentation of white matter lesions (WML), a magnetic resonance imaging (MRI) biomarker in multiple sclerosis (MS). 687 patients diagnosed with MS for a total of 4043 FLAIR and MPRAGE MRI scans were collected at the University Hospital of Basel, Switzerland. Data were randomly split into training, validation and test sets to train a 3D U-Net for MS lesion segmentation. We observed 3050 true positive (TP), 1818 false positive (FP), and 789 false negative (FN) cases. We generated instance-level explanation maps for semantic segmentation, by developing two XAI methods based on SmoothGrad and Grad-CAM++. We investigated: 1) the distribution of gradients in saliency maps with respect to both input MRI sequences; 2) the model's response in the case of synthetic lesions; 3) the amount of perilesional tissue needed by the model to segment a lesion. Saliency maps (based on SmoothGrad) in FLAIR showed positive values inside a lesion and negative in its neighborhood. Peak values of saliency maps generated for these four groups of volumes presented distributions that differ significantly from one another, suggesting a quantitative nature of the proposed saliency. Contextual information of 7mm around the lesion border was required for their segmentation.
|
http://arxiv.org/pdf/2406.09335v2
|
[
"Federico Spagnolo",
"Nataliia Molchanova",
"Roger Schaer",
"Meritxell Bach Cuadra",
"Mario Ocampo Pineda",
"Lester Melie-Garcia",
"Cristina Granziera",
"Vincent Andrearczyk",
"Adrien Depeursinge"
] |
2024-06-25T13:47:06Z
|
2024-06-13T17:17:31Z
|
2403.07095
|
Overcoming the Paradox of Certified Training with Gaussian Smoothing
|
Training neural networks with high certified accuracy against adversarial examples remains an open problem despite significant efforts. While certification methods can effectively leverage tight convex relaxations for bound computation, in training, these methods perform worse than looser relaxations. Prior work hypothesized that this is caused by the discontinuity and perturbation sensitivity of the loss surface induced by these tighter relaxations. In this work, we show theoretically that Gaussian Loss Smoothing can alleviate both issues. We confirm this empirically by proposing a certified training method combining PGPE, an algorithm computing gradients of a smoothed loss, with different convex relaxations. When using this training method, we observe that tighter bounds indeed lead to strictly better networks. While scaling PGPE training remains challenging due to high computational cost, we show that by using a not theoretically sound, yet much cheaper smoothing approximation, we obtain better certified accuracies than state-of-the-art methods when training on the same network architecture. Our results clearly demonstrate the promise of Gaussian Loss Smoothing for training certifiably robust neural networks.
|
http://arxiv.org/pdf/2403.07095v2
|
[
"Stefan Balauca",
"Mark Niklas Müller",
"Yuhao Mao",
"Maximilian Baader",
"Marc Fischer",
"Martin Vechev"
] |
2024-06-25T13:46:24Z
|
2024-03-11T18:44:36Z
|
2402.11253
|
Aligning Large Language Models by On-Policy Self-Judgment
|
Existing approaches for aligning large language models with human preferences face a trade-off that requires a separate reward model (RM) for on-policy learning. In this paper, we present a novel alignment framework, SELF-JUDGE that (1) does on-policy learning and 2) is parameter efficient, as it does not require an additional RM for evaluating the samples for on-policy learning. To this end, we propose Judge-augmented Supervised Fine-Tuning (JSFT) to train a single model to act as both a policy and a judge. Specifically, we view the pairwise judgment task, choosing the better response from a response pair, as a special case of the instruction-following task. The resulting model can judge preferences of on-the-fly responses from current policy initialized from itself. Experimental results show the efficacy of SELF-JUDGE, outperforming baselines in preference benchmarks. We also show that the rejecting sampling by itself can improve performance further without an additional evaluator.
|
http://arxiv.org/pdf/2402.11253v3
|
[
"Sangkyu Lee",
"Sungdong Kim",
"Ashkan Yousefpour",
"Minjoon Seo",
"Kang Min Yoo",
"Youngjae Yu"
] |
2024-06-25T13:39:52Z
|
2024-02-17T11:25:26Z
|
2401.02283
|
DEM: A Method for Certifying Deep Neural Network Classifier Outputs in
Aerospace
|
Software development in the aerospace domain requires adhering to strict, high-quality standards. While there exist regulatory guidelines for commercial software in this domain (e.g., ARP-4754 and DO-178), these do not apply to software with deep neural network (DNN) components. Consequently, it is unclear how to allow aerospace systems to benefit from the deep learning revolution. Our work here seeks to address this challenge with a novel, output-centric approach for DNN certification. Our method employs statistical verification techniques, and has the key advantage of being able to flag specific inputs for which the DNN's output may be unreliable - so that they may be later inspected by a human expert. To achieve this, our method conducts a statistical analysis of the DNN's predictions for other, nearby inputs, in order to detect inconsistencies. This is in contrast to existing techniques, which typically attempt to certify the entire DNN, as opposed to individual outputs. Our method uses the DNN as a black-box, and makes no assumptions about its topology. We hope that this work constitutes another step towards integrating DNNs in safety-critical applications - especially in the aerospace domain, where high standards of quality and reliability are crucial.
|
http://arxiv.org/pdf/2401.02283v3
|
[
"Guy Katz",
"Natan Levy",
"Idan Refaeli",
"Raz Yerushalmi"
] |
2024-06-25T13:35:13Z
|
2024-01-04T14:01:24Z
|
2406.09346
|
Scoreformer: A Surrogate Model For Large-Scale Prediction of Docking
Scores
|
In this study, we present ScoreFormer, a novel graph transformer model designed to accurately predict molecular docking scores, thereby optimizing high-throughput virtual screening (HTVS) in drug discovery. The architecture integrates Principal Neighborhood Aggregation (PNA) and Learnable Random Walk Positional Encodings (LRWPE), enhancing the model's ability to understand complex molecular structures and their relationship with their respective docking scores. This approach significantly surpasses traditional HTVS methods and recent Graph Neural Network (GNN) models in both recovery and efficiency due to a wider coverage of the chemical space and enhanced performance. Our results demonstrate that ScoreFormer achieves competitive performance in docking score prediction and offers a substantial 1.65-fold reduction in inference time compared to existing models. We evaluated ScoreFormer across multiple datasets under various conditions, confirming its robustness and reliability in identifying potential drug candidates rapidly.
|
http://arxiv.org/pdf/2406.09346v2
|
[
"Álvaro Ciudad",
"Adrián Morales-Pastor",
"Laura Malo",
"Isaac Filella-Mercè",
"Victor Guallar",
"Alexis Molina"
] |
2024-06-25T13:25:08Z
|
2024-06-13T17:31:02Z
|
2406.17826
|
European Space Agency Benchmark for Anomaly Detection in Satellite
Telemetry
|
Machine learning has vast potential to improve anomaly detection in satellite telemetry which is a crucial task for spacecraft operations. This potential is currently hampered by a lack of comprehensible benchmarks for multivariate time series anomaly detection, especially for the challenging case of satellite telemetry. The European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry (ESA-ADB) aims to address this challenge and establish a new standard in the domain. It is a result of close cooperation between spacecraft operations engineers from the European Space Agency (ESA) and machine learning experts. The newly introduced ESA Anomalies Dataset contains annotated real-life telemetry from three different ESA missions, out of which two are included in ESA-ADB. Results of typical anomaly detection algorithms assessed in our novel hierarchical evaluation pipeline show that new approaches are necessary to address operators' needs. All elements of ESA-ADB are publicly available to ensure its full reproducibility.
|
http://arxiv.org/pdf/2406.17826v1
|
[
"Krzysztof Kotowski",
"Christoph Haskamp",
"Jacek Andrzejewski",
"Bogdan Ruszczak",
"Jakub Nalepa",
"Daniel Lakey",
"Peter Collins",
"Aybike Kolmas",
"Mauro Bartesaghi",
"Jose Martinez-Heras",
"Gabriele De Canio"
] |
2024-06-25T13:23:37Z
|
2024-06-25T13:23:37Z
|
2406.18612
|
Optimal spanning tree reconstruction in symbolic regression
|
This paper investigates the problem of regression model generation. A model is a superposition of primitive functions. The model structure is described by a weighted colored graph. Each graph vertex corresponds to some primitive function. An edge assigns a superposition of two functions. The weight of an edge equals the probability of superposition. To generate an optimal model one has to reconstruct its structure from its graph adjacency matrix. The proposed algorithm reconstructs the~minimum spanning tree from the~weighted colored graph. This paper presents a novel solution based on the prize-collecting Steiner tree algorithm. This algorithm is compared with its alternatives.
|
http://arxiv.org/pdf/2406.18612v1
|
[
"Radoslav G. Neychev",
"Innokentiy A. Shibaev",
"Vadim V. Strijov"
] |
2024-06-25T13:22:13Z
|
2024-06-25T13:22:13Z
|
2406.17537
|
SincVAE: a New Approach to Improve Anomaly Detection on EEG Data Using
SincNet and Variational Autoencoder
|
Over the past few decades, electroencephalography (EEG) monitoring has become a pivotal tool for diagnosing neurological disorders, particularly for detecting seizures. Epilepsy, one of the most prevalent neurological diseases worldwide, affects approximately the 1 % of the population. These patients face significant risks, underscoring the need for reliable, continuous seizure monitoring in daily life. Most of the techniques discussed in the literature rely on supervised Machine Learning (ML) methods. However, the challenge of accurately labeling variations in epileptic EEG waveforms complicates the use of these approaches. Additionally, the rarity of ictal events introduces an high imbalancing within the data, which could lead to poor prediction performance in supervised learning approaches. Instead, a semi-supervised approach allows to train the model only on data not containing seizures, thus avoiding the issues related to the data imbalancing. This work proposes a semi-supervised approach for detecting epileptic seizures from EEG data, utilizing a novel Deep Learning-based method called SincVAE. This proposal incorporates the learning of an ad-hoc array of bandpass filter as a first layer of a Variational Autoencoder (VAE), potentially eliminating the preprocessing stage where informative band frequencies are identified and isolated. Results indicate that SincVAE improves seizure detection in EEG data and is capable of identifying early seizures during the preictal stage as well as monitoring patients throughout the postictal stage.
|
http://arxiv.org/pdf/2406.17537v1
|
[
"Andrea Pollastro",
"Francesco Isgrò",
"Roberto Prevete"
] |
2024-06-25T13:21:01Z
|
2024-06-25T13:21:01Z
|
2402.02111
|
Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo
is All you Need
|
We leverage multilevel Monte Carlo (MLMC) to improve the performance of multi-step look-ahead Bayesian optimization (BO) methods that involve nested expectations and maximizations. Often these expectations must be computed by Monte Carlo (MC). The complexity rate of naive MC degrades for nested operations, whereas MLMC is capable of achieving the canonical MC convergence rate for this type of problem, independently of dimension and without any smoothness assumptions. Our theoretical study focuses on the approximation improvements for twoand three-step look-ahead acquisition functions, but, as we discuss, the approach is generalizable in various ways, including beyond the context of BO. Our findings are verified numerically and the benefits of MLMC for BO are illustrated on several benchmark examples. Code is available at https://github.com/Shangda-Yang/MLMCBO .
|
http://arxiv.org/pdf/2402.02111v2
|
[
"Shangda Yang",
"Vitaly Zankin",
"Maximilian Balandat",
"Stefan Scherer",
"Kevin Carlberg",
"Neil Walton",
"Kody J. H. Law"
] |
2024-06-25T13:11:33Z
|
2024-02-03T10:24:30Z
|
2402.12479
|
In value-based deep reinforcement learning, a pruned network is a good
network
|
Recent work has shown that deep reinforcement learning agents have difficulty in effectively using their network parameters. We leverage prior insights into the advantages of sparse training techniques and demonstrate that gradual magnitude pruning enables value-based agents to maximize parameter effectiveness. This results in networks that yield dramatic performance improvements over traditional networks, using only a small fraction of the full network parameters.
|
http://arxiv.org/pdf/2402.12479v3
|
[
"Johan Obando-Ceron",
"Aaron Courville",
"Pablo Samuel Castro"
] |
2024-06-25T13:10:06Z
|
2024-02-19T19:34:07Z
|
2311.00787
|
Accelerating Electronic Stopping Power Predictions by 10 Million Times
with a Combination of Time-Dependent Density Functional Theory and Machine
Learning
|
Knowing the rate at which particle radiation releases energy in a material, the stopping power, is key to designing nuclear reactors, medical treatments, semiconductor and quantum materials, and many other technologies. While the nuclear contribution to stopping power, i.e., elastic scattering between atoms, is well understood in the literature, the route for gathering data on the electronic contribution has for decades remained costly and reliant on many simplifying assumptions, including that materials are isotropic. We establish a method that combines time-dependent density functional theory (TDDFT) and machine learning to reduce the time to assess new materials to mere hours on a supercomputer and provides valuable data on how atomic details influence electronic stopping. Our approach uses TDDFT to compute the electronic stopping contributions to stopping power from first principles in several directions and then machine learning to interpolate to other directions at a cost of 10 million times fewer core-hours. We demonstrate the combined approach in a study of proton irradiation in aluminum and employ it to predict how the depth of maximum energy deposition, the "Bragg Peak," varies depending on incident angle -- a quantity otherwise inaccessible to modelers. The lack of any experimental information requirement makes our method applicable to most materials, and its speed makes it a prime candidate for enabling quantum-to-continuum models of radiation damage. The prospect of reusing valuable TDDFT data for training the model make our approach appealing for applications in the age of materials data science.
|
http://arxiv.org/pdf/2311.00787v2
|
[
"Logan Ward",
"Ben Blaiszik",
"Cheng-Wei Lee",
"Troy Martin",
"Ian Foster",
"André Schleife"
] |
2024-06-25T13:09:23Z
|
2023-11-01T19:11:46Z
|
2308.13049
|
Bayesian Exploration Networks
|
Bayesian reinforcement learning (RL) offers a principled and elegant approach for sequential decision making under uncertainty. Most notably, Bayesian agents do not face an exploration/exploitation dilemma, a major pathology of frequentist methods. However theoretical understanding of model-free approaches is lacking. In this paper, we introduce a novel Bayesian model-free formulation and the first analysis showing that model-free approaches can yield Bayes-optimal policies. We show all existing model-free approaches make approximations that yield policies that can be arbitrarily Bayes-suboptimal. As a first step towards model-free Bayes optimality, we introduce the Bayesian exploration network (BEN) which uses normalising flows to model both the aleatoric uncertainty (via density estimation) and epistemic uncertainty (via variational inference) in the Bellman operator. In the limit of complete optimisation, BEN learns true Bayes-optimal policies, but like in variational expectation-maximisation, partial optimisation renders our approach tractable. Empirical results demonstrate that BEN can learn true Bayes-optimal policies in tasks where existing model-free approaches fail.
|
http://arxiv.org/pdf/2308.13049v4
|
[
"Mattie Fellows",
"Brandon Kaplowitz",
"Christian Schroeder de Witt",
"Shimon Whiteson"
] |
2024-06-25T13:06:13Z
|
2023-08-24T19:35:58Z
|
2406.17517
|
Preserving Node Distinctness in Graph Autoencoders via Similarity
Distillation
|
Graph autoencoders (GAEs), as a kind of generative self-supervised learning approach, have shown great potential in recent years. GAEs typically rely on distance-based criteria, such as mean-square-error (MSE), to reconstruct the input graph. However, relying solely on a single reconstruction criterion may lead to a loss of distinctiveness in the reconstructed graph, causing nodes to collapse into similar representations and resulting in sub-optimal performance. To address this issue, we have developed a simple yet effective strategy to preserve the necessary distinctness in the reconstructed graph. Inspired by the knowledge distillation technique, we found that the dual encoder-decoder architecture of GAEs can be viewed as a teacher-student relationship. Therefore, we propose transferring the knowledge of distinctness from the raw graph to the reconstructed graph, achieved through a simple KL constraint. Specifically, we compute pairwise node similarity scores in the raw graph and reconstructed graph. During the training process, the KL constraint is optimized alongside the reconstruction criterion. We conducted extensive experiments across three types of graph tasks, demonstrating the effectiveness and generality of our strategy. This indicates that the proposed approach can be employed as a plug-and-play method to avoid vague reconstructions and enhance overall performance.
|
http://arxiv.org/pdf/2406.17517v1
|
[
"Ge Chen",
"Yulan Hu",
"Sheng Ouyang",
"Yong Liu",
"Cuicui Luo"
] |
2024-06-25T12:54:35Z
|
2024-06-25T12:54:35Z
|
2310.08574
|
Jigsaw: Supporting Designers to Prototype Multimodal Applications by
Chaining AI Foundation Models
|
Recent advancements in AI foundation models have made it possible for them to be utilized off-the-shelf for creative tasks, including ideating design concepts or generating visual prototypes. However, integrating these models into the creative process can be challenging as they often exist as standalone applications tailored to specific tasks. To address this challenge, we introduce Jigsaw, a prototype system that employs puzzle pieces as metaphors to represent foundation models. Jigsaw allows designers to combine different foundation model capabilities across various modalities by assembling compatible puzzle pieces. To inform the design of Jigsaw, we interviewed ten designers and distilled design goals. In a user study, we showed that Jigsaw enhanced designers' understanding of available foundation model capabilities, provided guidance on combining capabilities across different modalities and tasks, and served as a canvas to support design exploration, prototyping, and documentation.
|
http://arxiv.org/abs/2310.08574v2
|
[
"David Chuan-En Lin",
"Nikolas Martelaro"
] |
2024-06-25T12:50:34Z
|
2023-10-12T17:57:57Z
|
2311.06062
|
Practical Membership Inference Attacks against Fine-tuned Large Language
Models via Self-prompt Calibration
|
Membership Inference Attacks (MIA) aim to infer whether a target data record has been utilized for model training or not. Prior attempts have quantified the privacy risks of language models (LMs) via MIAs, but there is still no consensus on whether existing MIA algorithms can cause remarkable privacy leakage on practical Large Language Models (LLMs). Existing MIAs designed for LMs can be classified into two categories: reference-free and reference-based attacks. They are both based on the hypothesis that training records consistently strike a higher probability of being sampled. Nevertheless, this hypothesis heavily relies on the overfitting of target models, which will be mitigated by multiple regularization methods and the generalization of LLMs. The reference-based attack seems to achieve promising effectiveness in LLMs, which measures a more reliable membership signal by comparing the probability discrepancy between the target model and the reference model. However, the performance of reference-based attack is highly dependent on a reference dataset that closely resembles the training dataset, which is usually inaccessible in the practical scenario. Overall, existing MIAs are unable to effectively unveil privacy leakage over practical fine-tuned LLMs that are overfitting-free and private. We propose a Membership Inference Attack based on Self-calibrated Probabilistic Variation (SPV-MIA). Specifically, since memorization in LLMs is inevitable during the training process and occurs before overfitting, we introduce a more reliable membership signal, probabilistic variation, which is based on memorization rather than overfitting. Furthermore, we introduce a self-prompt approach, which constructs the dataset to fine-tune the reference model by prompting the target LLM itself. In this manner, the adversary can collect a dataset with a similar distribution from public APIs.
|
http://arxiv.org/pdf/2311.06062v3
|
[
"Wenjie Fu",
"Huandong Wang",
"Chen Gao",
"Guanghua Liu",
"Yong Li",
"Tao Jiang"
] |
2024-06-25T12:36:02Z
|
2023-11-10T13:55:05Z
|
2308.12143
|
A Probabilistic Fluctuation based Membership Inference Attack for
Diffusion Models
|
Membership Inference Attack (MIA) identifies whether a record exists in a machine learning model's training set by querying the model. MIAs on the classic classification models have been well-studied, and recent works have started to explore how to transplant MIA onto generative models. Our investigation indicates that existing MIAs designed for generative models mainly depend on the overfitting in target models. However, overfitting can be avoided by employing various regularization techniques, whereas existing MIAs demonstrate poor performance in practice. Unlike overfitting, memorization is essential for deep learning models to attain optimal performance, making it a more prevalent phenomenon. Memorization in generative models leads to an increasing trend in the probability distribution of generating records around the member record. Therefore, we propose a Probabilistic Fluctuation Assessing Membership Inference Attack (PFAMI), a black-box MIA that infers memberships by detecting these trends via analyzing the overall probabilistic fluctuations around given records. We conduct extensive experiments across multiple generative models and datasets, which demonstrate PFAMI can improve the attack success rate (ASR) by about 27.9% when compared with the best baseline.
|
http://arxiv.org/pdf/2308.12143v4
|
[
"Wenjie Fu",
"Huandong Wang",
"Chen Gao",
"Guanghua Liu",
"Yong Li",
"Tao Jiang"
] |
2024-06-25T12:34:46Z
|
2023-08-23T14:00:58Z
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.