categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
sequence |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2407.03925 | null | null | http://arxiv.org/pdf/2407.03925v1 | 2024-07-04T13:37:26Z | 2024-07-04T13:37:26Z | Reduced-Order Neural Operators: Learning Lagrangian Dynamics on Highly
Sparse Graphs | We present a neural operator architecture to simulate Lagrangian dynamics, such as fluid flow, granular flows, and elastoplasticity. Traditional numerical methods, such as the finite element method (FEM), suffer from long run times and large memory consumption. On the other hand, approaches based on graph neural networks are faster but still suffer from long computation times on dense graphs, which are often required for high-fidelity simulations. Our model, GIOROM or Graph Interaction Operator for Reduced-Order Modeling, learns temporal dynamics within a reduced-order setting, capturing spatial features from a highly sparse graph representation of the input and generalizing to arbitrary spatial locations during inference. The model is geometry-aware and discretization-agnostic and can generalize to different initial conditions, velocities, and geometries after training. We show that point clouds of the order of 100,000 points can be inferred from sparse graphs with $sim$1000 points, with negligible change in computation time. We empirically evaluate our model on elastic solids, Newtonian fluids, Non-Newtonian fluids, Drucker-Prager granular flows, and von Mises elastoplasticity. On these benchmarks, our approach results in a 25$times$ speedup compared to other neural network-based physics simulators while delivering high-fidelity predictions of complex physical systems and showing better performance on most benchmarks. The code and the demos are provided at https://github.com/HrishikeshVish/GIOROM. | [
"['Hrishikesh Viswanath' 'Yue Chang' 'Julius Berner' 'Peter Yichen Chen'\n 'Aniket Bera']"
] |
null | null | 2407.03945 | null | null | http://arxiv.org/pdf/2407.03945v1 | 2024-07-04T14:02:10Z | 2024-07-04T14:02:10Z | A fast neural hybrid Newton solver adapted to implicit methods for
nonlinear dynamics | The use of implicit time-stepping schemes for the numerical approximation of solutions to stiff nonlinear time-evolution equations brings well-known advantages including, typically, better stability behaviour and corresponding support of larger time steps, and better structure preservation properties. However, this comes at the price of having to solve a nonlinear equation at every time step of the numerical scheme. In this work, we propose a novel operator learning based hybrid Newton's method to accelerate this solution of the nonlinear time step system for stiff time-evolution nonlinear equations. We propose a targeted learning strategy which facilitates robust unsupervised learning in an offline phase and provides a highly efficient initialisation for the Newton iteration leading to consistent acceleration of Newton's method. A quantifiable rate of improvement in Newton's method achieved by improved initialisation is provided and we analyse the upper bound of the generalisation error of our unsupervised learning strategy. These theoretical results are supported by extensive numerical results, demonstrating the efficiency of our proposed neural hybrid solver both in one- and two-dimensional cases. | [
"['Tianyu Jin' 'Georg Maierhofer' 'Katharina Schratz' 'Yang Xiang']"
] |
null | null | 2407.03951 | null | null | http://arxiv.org/pdf/2407.03951v1 | 2024-07-04T14:08:50Z | 2024-07-04T14:08:50Z | Uncertainty-Guided Optimization on Large Language Model Search Trees | Beam search is a standard tree search algorithm when it comes to finding sequences of maximum likelihood, for example, in the decoding processes of large language models. However, it is myopic since it does not take the whole path from the root to a leaf into account. Moreover, it is agnostic to prior knowledge available about the process: For example, it does not consider that the objective being maximized is a likelihood and thereby has specific properties, like being bound in the unit interval. Taking a probabilistic approach, we define a prior belief over the LLMs' transition probabilities and obtain a posterior belief over the most promising paths in each iteration. These beliefs are helpful to define a non-myopic Bayesian-optimization-like acquisition function that allows for a more data-efficient exploration scheme than standard beam search. We discuss how to select the prior and demonstrate in on- and off-model experiments with recent large language models, including Llama-2-7b, that our method achieves higher efficiency than beam search: Our method achieves the same or a higher likelihood while expanding fewer nodes than beam search. | [
"['Julia Grosse' 'Ruotian Wu' 'Ahmad Rashid' 'Philipp Hennig'\n 'Pascal Poupart' 'Agustinus Kristiadi']"
] |
null | null | 2407.03953 | null | null | http://arxiv.org/pdf/2407.03953v1 | 2024-07-04T14:14:09Z | 2024-07-04T14:14:09Z | Generalizing Graph Transformers Across Diverse Graphs and Tasks via
Pre-Training on Industrial-Scale Data | Graph pre-training has been concentrated on graph-level on small graphs (e.g., molecular graphs) or learning node representations on a fixed graph. Extending graph pre-trained models to web-scale graphs with billions of nodes in industrial scenarios, while avoiding negative transfer across graphs or tasks, remains a challenge. We aim to develop a general graph pre-trained model with inductive ability that can make predictions for unseen new nodes and even new graphs. In this work, we introduce a scalable transformer-based graph pre-training framework called PGT (Pre-trained Graph Transformer). Specifically, we design a flexible and scalable graph transformer as the backbone network. Meanwhile, based on the masked autoencoder architecture, we design two pre-training tasks: one for reconstructing node features and the other one for reconstructing local structures. Unlike the original autoencoder architecture where the pre-trained decoder is discarded, we propose a novel strategy that utilizes the decoder for feature augmentation. We have deployed our framework on Tencent's online game data. Extensive experiments have demonstrated that our framework can perform pre-training on real-world web-scale graphs with over 540 million nodes and 12 billion edges and generalizes effectively to unseen new graphs with different downstream tasks. We further conduct experiments on the publicly available ogbn-papers100M dataset, which consists of 111 million nodes and 1.6 billion edges. Our framework achieves state-of-the-art performance on both industrial datasets and public datasets, while also enjoying scalability and efficiency. | [
"['Yufei He' 'Zhenyu Hou' 'Yukuo Cen' 'Feng He' 'Xu Cheng' 'Bryan Hooi']"
] |
null | null | 2407.03961 | null | null | http://arxiv.org/pdf/2407.03961v2 | 2024-07-11T14:14:22Z | 2024-07-04T14:28:52Z | Leveraging Latent Diffusion Models for Training-Free In-Distribution
Data Augmentation for Surface Defect Detection | Defect detection is the task of identifying defects in production samples. Usually, defect detection classifiers are trained on ground-truth data formed by normal samples (negative data) and samples with defects (positive data), where the latter are consistently fewer than normal samples. State-of-the-art data augmentation procedures add synthetic defect data by superimposing artifacts to normal samples to mitigate problems related to unbalanced training data. These techniques often produce out-of-distribution images, resulting in systems that learn what is not a normal sample but cannot accurately identify what a defect looks like. In this work, we introduce DIAG, a training-free Diffusion-based In-distribution Anomaly Generation pipeline for data augmentation. Unlike conventional image generation techniques, we implement a human-in-the-loop pipeline, where domain experts provide multimodal guidance to the model through text descriptions and region localization of the possible anomalies. This strategic shift enhances the interpretability of results and fosters a more robust human feedback loop, facilitating iterative improvements of the generated outputs. Remarkably, our approach operates in a zero-shot manner, avoiding time-consuming fine-tuning procedures while achieving superior performance. We demonstrate the efficacy and versatility of DIAG with respect to state-of-the-art data augmentation approaches on the challenging KSDD2 dataset, with an improvement in AP of approximately 18% when positive samples are available and 28% when they are missing. The source code is available at https://github.com/intelligolabs/DIAG. | [
"['Federico Girella' 'Ziyue Liu' 'Franco Fummi' 'Francesco Setti'\n 'Marco Cristani' 'Luigi Capogrosso']"
] |
null | null | 2407.03964 | null | null | http://arxiv.org/pdf/2407.03964v1 | 2024-07-04T14:33:47Z | 2024-07-04T14:33:47Z | Improving Sample Efficiency of Reinforcement Learning with Background
Knowledge from Large Language Models | Low sample efficiency is an enduring challenge of reinforcement learning (RL). With the advent of versatile large language models (LLMs), recent works impart common-sense knowledge to accelerate policy learning for RL processes. However, we note that such guidance is often tailored for one specific task but loses generalizability. In this paper, we introduce a framework that harnesses LLMs to extract background knowledge of an environment, which contains general understandings of the entire environment, making various downstream RL tasks benefit from one-time knowledge representation. We ground LLMs by feeding a few pre-collected experiences and requesting them to delineate background knowledge of the environment. Afterward, we represent the output knowledge as potential functions for potential-based reward shaping, which has a good property for maintaining policy optimality from task rewards. We instantiate three variants to prompt LLMs for background knowledge, including writing code, annotating preferences, and assigning goals. Our experiments show that these methods achieve significant sample efficiency improvements in a spectrum of downstream tasks from Minigrid and Crafter domains. | [
"['Fuxiang Zhang' 'Junyou Li' 'Yi-Chen Li' 'Zongzhang Zhang' 'Yang Yu'\n 'Deheng Ye']"
] |
null | null | 2407.03979 | null | null | http://arxiv.org/pdf/2407.03979v1 | 2024-07-04T14:51:10Z | 2024-07-04T14:51:10Z | Zero-failure testing of binary classifiers | We propose using performance metrics derived from zero-failure testing to assess binary classifiers. The principal characteristic of the proposed approach is the asymmetric treatment of the two types of error. In particular, we construct a test set consisting of positive and negative samples, set the operating point of the binary classifier at the lowest value that will result to correct classifications of all positive samples, and use the algorithm's success rate on the negative samples as a performance measure. A property of the proposed approach, setting it apart from other commonly used testing methods, is that it allows the construction of a series of tests of increasing difficulty, corresponding to a nested sequence of positive sample test sets. We illustrate the proposed method on the problem of age estimation for determining whether a subject is above a legal age threshold, a problem that exemplifies the asymmetry of the two types of error. Indeed, misclassifying an under-aged subject is a legal and regulatory issue, while misclassifications of people above the legal age is an efficiency issue primarily concerning the commercial user of the age estimation system. | [
"['Ioannis Ivrissimtzis' 'Matthew Houliston' 'Shauna Concannon'\n 'Graham Roberts']"
] |
null | null | 2407.03995 | null | null | http://arxiv.org/pdf/2407.03995v1 | 2024-07-04T15:14:57Z | 2024-07-04T15:14:57Z | ROER: Regularized Optimal Experience Replay | Experience replay serves as a key component in the success of online reinforcement learning (RL). Prioritized experience replay (PER) reweights experiences by the temporal difference (TD) error empirically enhancing the performance. However, few works have explored the motivation of using TD error. In this work, we provide an alternative perspective on TD-error-based reweighting. We show the connections between the experience prioritization and occupancy optimization. By using a regularized RL objective with $f-$divergence regularizer and employing its dual form, we show that an optimal solution to the objective is obtained by shifting the distribution of off-policy data in the replay buffer towards the on-policy optimal distribution using TD-error-based occupancy ratios. Our derivation results in a new pipeline of TD error prioritization. We specifically explore the KL divergence as the regularizer and obtain a new form of prioritization scheme, the regularized optimal experience replay (ROER). We evaluate the proposed prioritization scheme with the Soft Actor-Critic (SAC) algorithm in continuous control MuJoCo and DM Control benchmark tasks where our proposed scheme outperforms baselines in 6 out of 11 tasks while the results of the rest match with or do not deviate far from the baselines. Further, using pretraining, ROER achieves noticeable improvement on difficult Antmaze environment where baselines fail, showing applicability to offline-to-online fine-tuning. Code is available at url{https://github.com/XavierChanglingLi/Regularized-Optimal-Experience-Replay}. | [
"['Changling Li' 'Zhang-Wei Hong' 'Pulkit Agrawal' 'Divyansh Garg'\n 'Joni Pajarinen']"
] |
null | null | 2407.04001 | null | null | http://arxiv.org/abs/2407.04001v1 | 2024-07-04T15:21:20Z | 2024-07-04T15:21:20Z | PaSE: Parallelization Strategies for Efficient DNN Training | Training a deep neural network (DNN) requires substantial computational and memory requirements. It is common to use multiple devices to train a DNN to reduce the overall training time. There are several choices to parallelize each layer in a DNN. Exhaustively searching this list to find an optimal parallelization strategy is prohibitively time consuming and impractical. The standard practice is to use data parallelism because of its simplicity. However, data parallelism is often sub-optimal, and suffers from poor performance and high memory requirement. Expert-designed strategies have been proposed on a case-by-case basis using domain specific knowledge. These expert-designed strategies do not generalize well to DNNs other than the ones for which they were designed, and are not always necessarily the best choice. In this paper, we propose an approach to automatically find efficient parallelization strategies for DNNs from their computation graphs. We present an efficient algorithm to compute these strategies within a reasonable time in practice. We evaluate the effectiveness of our approach on various DNNs. We also compare the performance of the strategies identified by our approach against data parallelism, expert-designed strategies, and the state-of-the-art approaches. Our results show that the strategies found using our approach outperform the baseline data parallelism strategy in all the cases. In addition, our strategies achieve better performance than the expert-designed strategies and the state-of-the-art approaches. | [
"['Venmugil Elango']"
] |
null | null | 2407.04009 | null | null | http://arxiv.org/pdf/2407.04009v1 | 2024-07-04T15:35:42Z | 2024-07-04T15:35:42Z | A Critical Assessment of Interpretable and Explainable Machine Learning
for Intrusion Detection | There has been a large number of studies in interpretable and explainable ML for cybersecurity, in particular, for intrusion detection. Many of these studies have significant amount of overlapping and repeated evaluations and analysis. At the same time, these studies overlook crucial model, data, learning process, and utility related issues and many times completely disregard them. These issues include the use of overly complex and opaque ML models, unaccounted data imbalances and correlated features, inconsistent influential features across different explanation methods, the inconsistencies stemming from the constituents of a learning process, and the implausible utility of explanations. In this work, we empirically demonstrate these issues, analyze them and propose practical solutions in the context of feature-based model explanations. Specifically, we advise avoiding complex opaque models such as Deep Neural Networks and instead using interpretable ML models such as Decision Trees as the available intrusion datasets are not difficult for such interpretable models to classify successfully. Then, we bring attention to the binary classification metrics such as Matthews Correlation Coefficient (which are well-suited for imbalanced datasets. Moreover, we find that feature-based model explanations are most often inconsistent across different settings. In this respect, to further gauge the extent of inconsistencies, we introduce the notion of cross explanations which corroborates that the features that are determined to be impactful by one explanation method most often differ from those by another method. Furthermore, we show that strongly correlated data features and the constituents of a learning process, such as hyper-parameters and the optimization routine, become yet another source of inconsistent explanations. Finally, we discuss the utility of feature-based explanations. | [
"['Omer Subasi' 'Johnathan Cree' 'Joseph Manzano' 'Elena Peterson']"
] |
null | null | 2407.04022 | null | null | http://arxiv.org/pdf/2407.04022v1 | 2024-07-04T16:01:21Z | 2024-07-04T16:01:21Z | Learning Non-Linear Invariants for Unsupervised Out-of-Distribution
Detection | The inability of deep learning models to handle data drawn from unseen distributions has sparked much interest in unsupervised out-of-distribution (U-OOD) detection, as it is crucial for reliable deep learning models. Despite considerable attention, theoretically-motivated approaches are few and far between, with most methods building on top of some form of heuristic. Recently, U-OOD was formalized in the context of data invariants, allowing a clearer understanding of how to characterize U-OOD, and methods leveraging affine invariants have attained state-of-the-art results on large-scale benchmarks. Nevertheless, the restriction to affine invariants hinders the expressiveness of the approach. In this work, we broaden the affine invariants formulation to a more general case and propose a framework consisting of a normalizing flow-like architecture capable of learning non-linear invariants. Our novel approach achieves state-of-the-art results on an extensive U-OOD benchmark, and we demonstrate its further applicability to tabular data. Finally, we show our method has the same desirable properties as those based on affine invariants. | [
"['Lars Doorenbos' 'Raphael Sznitman' 'Pablo Márquez-Neila']"
] |
null | null | 2407.04029 | null | null | http://arxiv.org/pdf/2407.04029v1 | 2024-07-04T16:13:25Z | 2024-07-04T16:13:25Z | Robust Learning under Hybrid Noise | Feature noise and label noise are ubiquitous in practical scenarios, which pose great challenges for training a robust machine learning model. Most previous approaches usually deal with only a single problem of either feature noise or label noise. However, in real-world applications, hybrid noise, which contains both feature noise and label noise, is very common due to the unreliable data collection and annotation processes. Although some results have been achieved by a few representation learning based attempts, this issue is still far from being addressed with promising performance and guaranteed theoretical analyses. To address the challenge, we propose a novel unified learning framework called "Feature and Label Recovery" (FLR) to combat the hybrid noise from the perspective of data recovery, where we concurrently reconstruct both the feature matrix and the label matrix of input data. Specifically, the clean feature matrix is discovered by the low-rank approximation, and the ground-truth label matrix is embedded based on the recovered features with a nuclear norm regularization. Meanwhile, the feature noise and label noise are characterized by their respective adaptive matrix norms to satisfy the corresponding maximum likelihood. As this framework leads to a non-convex optimization problem, we develop the non-convex Alternating Direction Method of Multipliers (ADMM) with the convergence guarantee to solve our learning objective. We also provide the theoretical analysis to show that the generalization error of FLR can be upper-bounded in the presence of hybrid noise. Experimental results on several typical benchmark datasets clearly demonstrate the superiority of our proposed method over the state-of-the-art robust learning approaches for various noises. | [
"['Yang Wei' 'Shuo Chen' 'Shanshan Ye' 'Bo Han' 'Chen Gong']"
] |
null | null | 2407.04055 | null | null | http://arxiv.org/pdf/2407.04055v1 | 2024-07-04T16:56:59Z | 2024-07-04T16:56:59Z | Benchmark on Drug Target Interaction Modeling from a Structure
Perspective | The prediction modeling of drug-target interactions is crucial to drug discovery and design, which has seen rapid advancements owing to deep learning technologies. Recently developed methods, such as those based on graph neural networks (GNNs) and Transformers, demonstrate exceptional performance across various datasets by effectively extracting structural information. However, the benchmarking of these novel methods often varies significantly in terms of hyperparameter settings and datasets, which limits algorithmic progress. In view of these, we conduct a comprehensive survey and benchmark for drug-target interaction modeling from a structure perspective, via integrating tens of explicit (i.e., GNN-based) and implicit (i.e., Transformer-based) structure learning algorithms. To this end, we first unify the hyperparameter setting within each class of structure learning methods. Moreover, we conduct a macroscopical comparison between these two classes of encoding strategies as well as the different featurization techniques that inform molecules' chemical and physical properties. We then carry out the microscopical comparison between all the integrated models across the six datasets, via comprehensively benchmarking their effectiveness and efficiency. Remarkably, the summarized insights from the benchmark studies lead to the design of model combos. We demonstrate that our combos can achieve new state-of-the-art performance on various datasets associated with cost-effective memory and computation. Our code is available at hyperlink{https://github.com/justinwjl/GTB-DTI/tree/main}{https://github.com/justinwjl/GTB-DTI/tree/main}. | [
"['Xinnan Zhang' 'Jialin Wu' 'Junyi Xie' 'Tianlong Chen' 'Kaixiong Zhou']"
] |
null | null | 2407.04057 | null | null | http://arxiv.org/pdf/2407.04057v1 | 2024-07-04T16:57:14Z | 2024-07-04T16:57:14Z | TALENT: A Tabular Analytics and Learning Toolbox | Tabular data is one of the most common data sources in machine learning. Although a wide range of classical methods demonstrate practical utilities in this field, deep learning methods on tabular data are becoming promising alternatives due to their flexibility and ability to capture complex interactions within the data. Considering that deep tabular methods have diverse design philosophies, including the ways they handle features, design learning objectives, and construct model architectures, we introduce a versatile deep-learning toolbox called TALENT (Tabular Analytics and LEarNing Toolbox) to utilize, analyze, and compare tabular methods. TALENT encompasses an extensive collection of more than 20 deep tabular prediction methods, associated with various encoding and normalization modules, and provides a unified interface that is easily integrable with new methods as they emerge. In this paper, we present the design and functionality of the toolbox, illustrate its practical application through several case studies, and investigate the performance of various methods fairly based on our toolbox. Code is available at https://github.com/qile2000/LAMDA-TALENT. | [
"['Si-Yang Liu' 'Hao-Run Cai' 'Qi-Le Zhou' 'Han-Jia Ye']"
] |
null | null | 2407.04065 | null | null | http://arxiv.org/pdf/2407.04065v2 | 2024-07-13T03:21:40Z | 2024-07-04T17:12:00Z | On the Workflows and Smells of Leaderboard Operations (LBOps): An
Exploratory Study of Foundation Model Leaderboards | Foundation models (FM), such as large language models (LLMs), which are large-scale machine learning (ML) models, have demonstrated remarkable adaptability in various downstream software engineering (SE) tasks, such as code completion, code understanding, and software development. As a result, FM leaderboards, especially those hosted on cloud platforms, have become essential tools for SE teams to compare and select the best third-party FMs for their specific products and purposes. However, the lack of standardized guidelines for FM evaluation and comparison threatens the transparency of FM leaderboards and limits stakeholders' ability to perform effective FM selection. As a first step towards addressing this challenge, our research focuses on understanding how these FM leaderboards operate in real-world scenarios ("leaderboard operations") and identifying potential leaderboard pitfalls and areas for improvement ("leaderboard smells"). In this regard, we perform a multivocal literature review to collect up to 721 FM leaderboards, after which we examine their documentation and engage in direct communication with leaderboard operators to understand their workflow patterns. Using card sorting and negotiated agreement, we identify 5 unique workflow patterns and develop a domain model that outlines the essential components and their interaction within FM leaderboards. We then identify 8 unique types of leaderboard smells in LBOps. By mitigating these smells, SE teams can improve transparency, accountability, and collaboration in current LBOps practices, fostering a more robust and responsible ecosystem for FM comparison and selection. | [
"['Zhimin Zhao' 'Abdul Ali Bangash' 'Filipe Roseiro Côgo' 'Bram Adams'\n 'Ahmed E. Hassan']"
] |
null | null | 2407.04069 | null | null | http://arxiv.org/pdf/2407.04069v1 | 2024-07-04T17:15:37Z | 2024-07-04T17:15:37Z | A Systematic Survey and Critical Review on Evaluating Large Language
Models: Challenges, Limitations, and Recommendations | Large Language Models (LLMs) have recently gained significant attention due to their remarkable capabilities in performing diverse tasks across various domains. However, a thorough evaluation of these models is crucial before deploying them in real-world applications to ensure they produce reliable performance. Despite the well-established importance of evaluating LLMs in the community, the complexity of the evaluation process has led to varied evaluation setups, causing inconsistencies in findings and interpretations. To address this, we systematically review the primary challenges and limitations causing these inconsistencies and unreliable evaluations in various steps of LLM evaluation. Based on our critical review, we present our perspectives and recommendations to ensure LLM evaluations are reproducible, reliable, and robust. | [
"['Md Tahmid Rahman Laskar' 'Sawsan Alqahtani' 'M Saiful Bari'\n 'Mizanur Rahman' 'Mohammad Abdullah Matin Khan' 'Haidar Khan'\n 'Israt Jahan' 'Amran Bhuiyan' 'Chee Wei Tan' 'Md Rizwan Parvez'\n 'Enamul Hoque' 'Shafiq Joty' 'Jimmy Huang']"
] |
null | null | 2407.04075 | null | null | http://arxiv.org/pdf/2407.04075v1 | 2024-07-04T17:33:15Z | 2024-07-04T17:33:15Z | Sparsest Models Elude Pruning: An Exposé of Pruning's Current
Capabilities | Pruning has emerged as a promising approach for compressing large-scale models, yet its effectiveness in recovering the sparsest of models has not yet been explored. We conducted an extensive series of 485,838 experiments, applying a range of state-of-the-art pruning algorithms to a synthetic dataset we created, named the Cubist Spiral. Our findings reveal a significant gap in performance compared to ideal sparse networks, which we identified through a novel combinatorial search algorithm. We attribute this performance gap to current pruning algorithms' poor behaviour under overparameterization, their tendency to induce disconnected paths throughout the network, and their propensity to get stuck at suboptimal solutions, even when given the optimal width and initialization. This gap is concerning, given the simplicity of the network architectures and datasets used in our study. We hope that our research encourages further investigation into new pruning techniques that strive for true network sparsity. | [
"['Stephen Zhang' 'Vardan Papyan']"
] |
null | null | 2407.04078 | null | null | http://arxiv.org/pdf/2407.04078v2 | 2024-07-09T15:29:03Z | 2024-07-04T17:39:16Z | DotaMath: Decomposition of Thought with Code Assistance and
Self-correction for Mathematical Reasoning | Large language models (LLMs) have made impressive progress in handling simple math problems, yet they still struggle with more challenging and complex mathematical tasks. In this paper, we introduce a series of LLMs that employs the Decomposition of thought with code assistance and self-correction for mathematical reasoning, dubbed as DotaMath. DotaMath models tackle complex mathematical tasks by decomposing them into simpler logical subtasks, leveraging code to solve these subtasks, obtaining fine-grained feedback from the code interpreter, and engaging in self-reflection and correction. By annotating diverse interactive tool-use trajectories and employing query evolution on GSM8K and MATH datasets, we generate an instruction fine-tuning dataset called DotaMathQA with 574K query-response pairs. We train a series of base LLMs using imitation learning on DotaMathQA, resulting in DotaMath models that achieve remarkable performance compared to open-source LLMs across various in-domain and out-of-domain benchmarks. Notably, DotaMath-deepseek-7B showcases an outstanding performance of 64.8% on the competitive MATH dataset and 86.7% on GSM8K. Besides, DotaMath-deepseek-7B maintains strong competitiveness on a series of in-domain and out-of-domain benchmarks (Avg. 80.1%). Looking forward, we anticipate that the DotaMath paradigm will open new pathways for addressing intricate mathematical problems. Our code is publicly available at https://github.com/ChengpengLi1003/DotaMath. | [
"['Chengpeng Li' 'Guanting Dong' 'Mingfeng Xue' 'Ru Peng' 'Xiang Wang'\n 'Dayiheng Liu']"
] |
null | null | 2407.04086 | null | null | http://arxiv.org/pdf/2407.04086v1 | 2024-07-04T17:56:04Z | 2024-07-04T17:56:04Z | Certifiably Robust Image Watermark | Generative AI raises many societal concerns such as boosting disinformation and propaganda campaigns. Watermarking AI-generated content is a key technology to address these concerns and has been widely deployed in industry. However, watermarking is vulnerable to removal attacks and forgery attacks. In this work, we propose the first image watermarks with certified robustness guarantees against removal and forgery attacks. Our method leverages randomized smoothing, a popular technique to build certifiably robust classifiers and regression models. Our major technical contributions include extending randomized smoothing to watermarking by considering its unique characteristics, deriving the certified robustness guarantees, and designing algorithms to estimate them. Moreover, we extensively evaluate our image watermarks in terms of both certified and empirical robustness. Our code is available at url{https://github.com/zhengyuan-jiang/Watermark-Library}. | [
"['Zhengyuan Jiang' 'Moyang Guo' 'Yuepeng Hu' 'Jinyuan Jia'\n 'Neil Zhenqiang Gong']"
] |
null | null | 2407.04104 | null | null | http://arxiv.org/pdf/2407.04104v1 | 2024-07-04T18:08:40Z | 2024-07-04T18:08:40Z | Network-based Neighborhood regression | Given the ubiquity of modularity in biological systems, module-level regulation analysis is vital for understanding biological systems across various levels and their dynamics. Current statistical analysis on biological modules predominantly focuses on either detecting the functional modules in biological networks or sub-group regression on the biological features without using the network data. This paper proposes a novel network-based neighborhood regression framework whose regression functions depend on both the global community-level information and local connectivity structures among entities. An efficient community-wise least square optimization approach is developed to uncover the strength of regulation among the network modules while enabling asymptotic inference. With random graph theory, we derive non-asymptotic estimation error bounds for the proposed estimator, achieving exact minimax optimality. Unlike the root-n consistency typical in canonical linear regression, our model exhibits linear consistency in the number of nodes n, highlighting the advantage of incorporating neighborhood information. The effectiveness of the proposed framework is further supported by extensive numerical experiments. Application to whole-exome sequencing and RNA-sequencing Autism datasets demonstrates the usage of the proposed method in identifying the association between the gene modules of genetic variations and the gene modules of genomic differential expressions. | [
"['Yaoming Zhen' 'Jin-Hong Du']"
] |
null | null | 2407.04108 | null | null | http://arxiv.org/pdf/2407.04108v1 | 2024-07-04T18:24:09Z | 2024-07-04T18:24:09Z | Future Events as Backdoor Triggers: Investigating Temporal
Vulnerabilities in LLMs | Backdoors are hidden behaviors that are only triggered once an AI system has been deployed. Bad actors looking to create successful backdoors must design them to avoid activation during training and evaluation. Since data used in these stages often only contains information about events that have already occurred, a component of a simple backdoor trigger could be a model recognizing data that is in the future relative to when it was trained. Through prompting experiments and by probing internal activations, we show that current large language models (LLMs) can distinguish past from future events, with probes on model activations achieving $90%$ accuracy. We train models with backdoors triggered by a temporal distributional shift; they activate when the model is exposed to news headlines beyond their training cut-off dates. Fine-tuning on helpful, harmless and honest (HHH) data does not work well for removing simpler backdoor triggers but is effective on our backdoored models, although this distinction is smaller for the larger-scale model we tested. We also find that an activation-steering vector representing a model's internal representation of the date influences the rate of backdoor activation. We take these results as initial evidence that, at least for models at the modest scale we test, standard safety measures are enough to remove these backdoors. We publicly release all relevant code (https://github.com/sbp354/Future_triggered_backdoors), datasets (https://tinyurl.com/future-backdoor-datasets), and models (https://huggingface.co/saraprice). | [
"['Sara Price' 'Arjun Panickssery' 'Sam Bowman' 'Asa Cooper Stickland']"
] |
null | null | 2407.04117 | null | null | http://arxiv.org/pdf/2407.04117v1 | 2024-07-04T18:39:20Z | 2024-07-04T18:39:20Z | Predictive Coding Networks and Inference Learning: Tutorial and Survey | Recent years have witnessed a growing call for renewed emphasis on neuroscience-inspired approaches in artificial intelligence research, under the banner of $textit{NeuroAI}$. This is exemplified by recent attention gained by predictive coding networks (PCNs) within machine learning (ML). PCNs are based on the neuroscientific framework of predictive coding (PC), which views the brain as a hierarchical Bayesian inference model that minimizes prediction errors from feedback connections. PCNs trained with inference learning (IL) have potential advantages to traditional feedforward neural networks (FNNs) trained with backpropagation. While historically more computationally intensive, recent improvements in IL have shown that it can be more efficient than backpropagation with sufficient parallelization, making PCNs promising alternatives for large-scale applications and neuromorphic hardware. Moreover, PCNs can be mathematically considered as a superset of traditional FNNs, which substantially extends the range of possible architectures for both supervised and unsupervised learning. In this work, we provide a comprehensive review as well as a formal specification of PCNs, in particular placing them in the context of modern ML methods, and positioning PC as a versatile and promising framework worthy of further study by the ML community. | [
"['Björn van Zwol' 'Ro Jefferson' 'Egon L. van den Broek']"
] |
null | null | 2407.04119 | null | null | http://arxiv.org/pdf/2407.04119v1 | 2024-07-04T18:40:50Z | 2024-07-04T18:40:50Z | An Autoencoder Architecture for L-band Passive Microwave Retrieval of
Landscape Freeze-Thaw Cycle | Estimating the landscape and soil freeze-thaw (FT) dynamics in the Northern Hemisphere is crucial for understanding permafrost response to global warming and changes in regional and global carbon budgets. A new framework is presented for surface FT-cycle retrievals using L-band microwave radiometry based on a deep convolutional autoencoder neural network. This framework defines the landscape FT-cycle retrieval as a time series anomaly detection problem considering the frozen states as normal and thawed states as anomalies. The autoencoder retrieves the FT-cycle probabilistically through supervised reconstruction of the brightness temperature (TB) time series using a contrastive loss function that minimizes (maximizes) the reconstruction error for the peak winter (summer). Using the data provided by the Soil Moisture Active Passive (SMAP) satellite, it is demonstrated that the framework learns to isolate the landscape FT states over different land surface types with varying complexities related to the radiometric characteristics of snow cover, lake-ice phenology, and vegetation canopy. The consistency of the retrievals is evaluated over Alaska, against in situ ground-based observations, showing reduced uncertainties compared to the traditional methods that use thresholding of the normalized polarization ratio. | [
"['Divya Kumawat' 'Ardeshir Ebtehaj' 'Xiaolan Xu' 'Andreas Colliander'\n 'Vipin Kumar']"
] |
null | null | 2407.04125 | null | null | http://arxiv.org/pdf/2407.04125v1 | 2024-07-04T18:54:30Z | 2024-07-04T18:54:30Z | Query-Guided Self-Supervised Summarization of Nursing Notes | Nursing notes, an important component of Electronic Health Records (EHRs), keep track of the progression of a patient's health status during a care episode. Distilling the key information in nursing notes through text summarization techniques can improve clinicians' efficiency in understanding patients' conditions when reviewing nursing notes. However, existing abstractive summarization methods in the clinical setting have often overlooked nursing notes and require the creation of reference summaries for supervision signals, which is time-consuming. In this work, we introduce QGSumm, a query-guided self-supervised domain adaptation framework for nursing note summarization. Using patient-related clinical queries as guidance, our approach generates high-quality, patient-centered summaries without relying on reference summaries for training. Through automatic and manual evaluation by an expert clinician, we demonstrate the strengths of our approach compared to the state-of-the-art Large Language Models (LLMs) in both zero-shot and few-shot settings. Ultimately, our approach provides a new perspective on conditional text summarization, tailored to the specific interests of clinical personnel. | [
"['Ya Gao' 'Hans Moen' 'Saila Koivusalo' 'Miika Koskinen' 'Pekka Marttinen']"
] |
null | null | 2407.04149 | null | null | http://arxiv.org/pdf/2407.04149v1 | 2024-07-04T20:53:19Z | 2024-07-04T20:53:19Z | SineKAN: Kolmogorov-Arnold Networks Using Sinusoidal Activation
Functions | Recent work has established an alternative to traditional multi-layer perceptron neural networks in the form of Kolmogorov-Arnold Networks (KAN). The general KAN framework uses learnable activation functions on the edges of the computational graph followed by summation on nodes. The learnable edge activation functions in the original implementation are basis spline functions (B-Spline). Here, we present a model in which learnable grids of B-Spline activation functions can be replaced by grids of re-weighted sine functions. We show that this leads to better or comparable numerical performance to B-Spline KAN models on the MNIST benchmark, while also providing a substantial speed increase on the order of 4-9 times. | [
"['Eric A. F. Reinhardt' 'Sergei Gleyzer']"
] |
null | null | 2407.04151 | null | null | http://arxiv.org/pdf/2407.04151v1 | 2024-07-04T20:57:06Z | 2024-07-04T20:57:06Z | Securing Multi-turn Conversational Language Models Against Distributed
Backdoor Triggers | The security of multi-turn conversational large language models (LLMs) is understudied despite it being one of the most popular LLM utilization. Specifically, LLMs are vulnerable to data poisoning backdoor attacks, where an adversary manipulates the training data to cause the model to output malicious responses to predefined triggers. Specific to the multi-turn dialogue setting, LLMs are at the risk of even more harmful and stealthy backdoor attacks where the backdoor triggers may span across multiple utterances, giving lee-way to context-driven attacks. In this paper, we explore a novel distributed backdoor trigger attack that serves to be an extra tool in an adversary's toolbox that can interface with other single-turn attack strategies in a plug and play manner. Results on two representative defense mechanisms indicate that distributed backdoor triggers are robust against existing defense strategies which are designed for single-turn user-model interactions, motivating us to propose a new defense strategy for the multi-turn dialogue setting that is more challenging. To this end, we also explore a novel contrastive decoding based defense that is able to mitigate the backdoor with a low computational tradeoff. | [
"['Terry Tong' 'Jiashu Xu' 'Qin Liu' 'Muhao Chen']"
] |
null | null | 2407.04152 | null | null | http://arxiv.org/pdf/2407.04152v1 | 2024-07-04T20:58:20Z | 2024-07-04T20:58:20Z | VoxAct-B: Voxel-Based Acting and Stabilizing Policy for Bimanual
Manipulation | Bimanual manipulation is critical to many robotics applications. In contrast to single-arm manipulation, bimanual manipulation tasks are challenging due to higher-dimensional action spaces. Prior works leverage large amounts of data and primitive actions to address this problem, but may suffer from sample inefficiency and limited generalization across various tasks. To this end, we propose VoxAct-B, a language-conditioned, voxel-based method that leverages Vision Language Models (VLMs) to prioritize key regions within the scene and reconstruct a voxel grid. We provide this voxel grid to our bimanual manipulation policy to learn acting and stabilizing actions. This approach enables more efficient policy learning from voxels and is generalizable to different tasks. In simulation, we show that VoxAct-B outperforms strong baselines on fine-grained bimanual manipulation tasks. Furthermore, we demonstrate VoxAct-B on real-world $texttt{Open Drawer}$ and $texttt{Open Jar}$ tasks using two UR5s. Code, data, and videos will be available at https://voxact-b.github.io. | [
"['I-Chun Arthur Liu' 'Sicheng He' 'Daniel Seita' 'Gaurav Sukhatme']"
] |
null | null | 2407.04153 | null | null | http://arxiv.org/pdf/2407.04153v1 | 2024-07-04T20:59:20Z | 2024-07-04T20:59:20Z | Mixture of A Million Experts | The feedforward (FFW) layers in standard transformer architectures incur a linear increase in computational costs and activation memory as the hidden layer width grows. Sparse mixture-of-experts (MoE) architectures have emerged as a viable approach to address this issue by decoupling model size from computational cost. The recent discovery of the fine-grained MoE scaling law shows that higher granularity leads to better performance. However, existing MoE models are limited to a small number of experts due to computational and optimization challenges. This paper introduces PEER (parameter efficient expert retrieval), a novel layer design that utilizes the product key technique for sparse retrieval from a vast pool of tiny experts (over a million). Experiments on language modeling tasks demonstrate that PEER layers outperform dense FFWs and coarse-grained MoEs in terms of performance-compute trade-off. By enabling efficient utilization of a massive number of experts, PEER unlocks the potential for further scaling of transformer models while maintaining computational efficiency. | [
"['Xu Owen He']"
] |
null | null | 2407.04157 | null | null | http://arxiv.org/pdf/2407.04157v1 | 2024-07-04T21:23:12Z | 2024-07-04T21:23:12Z | Finite Operator Learning: Bridging Neural Operators and Numerical
Methods for Efficient Parametric Solution and Optimization of PDEs | We introduce a method that combines neural operators, physics-informed machine learning, and standard numerical methods for solving PDEs. The proposed approach extends each of the aforementioned methods and unifies them within a single framework. We can parametrically solve partial differential equations in a data-free manner and provide accurate sensitivities, meaning the derivatives of the solution space with respect to the design space. These capabilities enable gradient-based optimization without the typical sensitivity analysis costs, unlike adjoint methods that scale directly with the number of response functions. Our Finite Operator Learning (FOL) approach uses an uncomplicated feed-forward neural network model to directly map the discrete design space (i.e. parametric input space) to the discrete solution space (i.e. finite number of sensor points in the arbitrary shape domain) ensuring compliance with physical laws by designing them into loss functions. The discretized governing equations, as well as the design and solution spaces, can be derived from any well-established numerical techniques. In this work, we employ the Finite Element Method (FEM) to approximate fields and their spatial derivatives. Subsequently, we conduct Sobolev training to minimize a multi-objective loss function, which includes the discretized weak form of the energy functional, boundary conditions violations, and the stationarity of the residuals with respect to the design variables. Our study focuses on the steady-state heat equation within heterogeneous materials that exhibits significant phase contrast and possibly temperature-dependent conductivity. The network's tangent matrix is directly used for gradient-based optimization to improve the microstructure's heat transfer characteristics. ... | [
"['Shahed Rezaei' 'Reza Najian Asl' 'Kianoosh Taghikhani'\n 'Ahmad Moeineddin' 'Michael Kaliske' 'Markus Apel']"
] |
null | null | 2407.04168 | null | null | http://arxiv.org/pdf/2407.04168v1 | 2024-07-04T21:58:26Z | 2024-07-04T21:58:26Z | Learning Interpretable Differentiable Logic Networks | The ubiquity of neural networks (NNs) in real-world applications, from healthcare to natural language processing, underscores their immense utility in capturing complex relationships within high-dimensional data. However, NNs come with notable disadvantages, such as their "black-box" nature, which hampers interpretability, as well as their tendency to overfit the training data. We introduce a novel method for learning interpretable differentiable logic networks (DLNs) that are architectures that employ multiple layers of binary logic operators. We train these networks by softening and differentiating their discrete components, e.g., through binarization of inputs, binary logic operations, and connections between neurons. This approach enables the use of gradient-based learning methods. Experimental results on twenty classification tasks indicate that differentiable logic networks can achieve accuracies comparable to or exceeding that of traditional NNs. Equally importantly, these networks offer the advantage of interpretability. Moreover, their relatively simple structure results in the number of logic gate-level operations during inference being up to a thousand times smaller than NNs, making them suitable for deployment on edge devices. | [
"['Chang Yue' 'Niraj K. Jha']"
] |
null | null | 2407.04173 | null | null | http://arxiv.org/pdf/2407.04173v1 | 2024-07-04T22:22:09Z | 2024-07-04T22:22:09Z | Quantifying Prediction Consistency Under Model Multiplicity in Tabular
LLMs | Fine-tuning large language models (LLMs) on limited tabular data for classification tasks can lead to textit{fine-tuning multiplicity}, where equally well-performing models make conflicting predictions on the same inputs due to variations in the training process (i.e., seed, random weight initialization, retraining on additional or deleted samples). This raises critical concerns about the robustness and reliability of Tabular LLMs, particularly when deployed for high-stakes decision-making, such as finance, hiring, education, healthcare, etc. This work formalizes the challenge of fine-tuning multiplicity in Tabular LLMs and proposes a novel metric to quantify the robustness of individual predictions without expensive model retraining. Our metric quantifies a prediction's stability by analyzing (sampling) the model's local behavior around the input in the embedding space. Interestingly, we show that sampling in the local neighborhood can be leveraged to provide probabilistic robustness guarantees against a broad class of fine-tuned models. By leveraging Bernstein's Inequality, we show that predictions with sufficiently high robustness (as defined by our measure) will remain consistent with high probability. We also provide empirical evaluation on real-world datasets to support our theoretical results. Our work highlights the importance of addressing fine-tuning instabilities to enable trustworthy deployment of LLMs in high-stakes and safety-critical applications. | [
"['Faisal Hamman' 'Pasan Dissanayake' 'Saumitra Mishra' 'Freddy Lecue'\n 'Sanghamitra Dutta']"
] |
null | null | 2407.04189 | null | null | http://arxiv.org/pdf/2407.04189v1 | 2024-07-04T23:47:10Z | 2024-07-04T23:47:10Z | Meta-Learning and representation learner: A short theoretical note | Meta-learning, or "learning to learn," is a subfield of machine learning where the goal is to develop models and algorithms that can learn from various tasks and improve their learning process over time. Unlike traditional machine learning methods focusing on learning a specific task, meta-learning aims to leverage experience from previous tasks to enhance future learning. This approach is particularly beneficial in scenarios where the available data for a new task is limited, but there exists abundant data from related tasks. By extracting and utilizing the underlying structure and patterns across these tasks, meta-learning algorithms can achieve faster convergence and better performance with fewer data. The following notes are mainly inspired from cite{vanschoren2018meta}, cite{baxter2019learning}, and cite{maurer2005algorithmic}. | [
"['Mouad El Bouchattaoui']"
] |
null | null | 2407.04192 | null | null | http://arxiv.org/pdf/2407.04192v1 | 2024-07-05T00:38:49Z | 2024-07-05T00:38:49Z | KAN-ODEs: Kolmogorov-Arnold Network Ordinary Differential Equations for
Learning Dynamical Systems and Hidden Physics | Kolmogorov-Arnold Networks (KANs) as an alternative to Multi-layer perceptrons (MLPs) are a recent development demonstrating strong potential for data-driven modeling. This work applies KANs as the backbone of a Neural Ordinary Differential Equation framework, generalizing their use to the time-dependent and grid-sensitive cases often seen in scientific machine learning applications. The proposed KAN-ODEs retain the flexible dynamical system modeling framework of Neural ODEs while leveraging the many benefits of KANs, including faster neural scaling, stronger interpretability, and lower parameter counts when compared against MLPs. We demonstrate these benefits in three test cases: the Lotka-Volterra predator-prey model, Burgers' equation, and the Fisher-KPP PDE. We showcase the strong performance of parameter-lean KAN-ODE systems generally in reconstructing entire dynamical systems, and also in targeted applications to the inference of a source term in an otherwise known flow field. We additionally demonstrate the interpretability of KAN-ODEs via activation function visualization and symbolic regression of trained results. The successful training of KAN-ODEs and their improved performance when compared to traditional Neural ODEs implies significant potential in leveraging this novel network architecture in myriad scientific machine learning applications. | [
"['Benjamin C. Koenig' 'Suyong Kim' 'Sili Deng']"
] |
null | null | 2407.04211 | null | null | http://arxiv.org/pdf/2407.04211v1 | 2024-07-05T01:47:20Z | 2024-07-05T01:47:20Z | TimeLDM: Latent Diffusion Model for Unconditional Time Series Generation | Time series generation is a crucial research topic in the area of deep learning, which can be used for data augmentation, imputing missing values, and forecasting. Currently, latent diffusion models are ascending to the forefront of generative modeling for many important data representations. Being the most pivotal in the computer vision domain, latent diffusion models have also recently attracted interest in other communities, including NLP, Speech, and Geometric Space. In this work, we propose TimeLDM, a novel latent diffusion model for high-quality time series generation. TimeLDM is composed of a variational autoencoder that encodes time series into an informative and smoothed latent content and a latent diffusion model operating in the latent space to generate latent information. We evaluate the ability of our method to generate synthetic time series with simulated and realistic datasets, benchmark the performance against existing state-of-the-art methods. Qualitatively and quantitatively, we find that the proposed TimeLDM persistently delivers high-quality generated time series. Sores from Context-FID and Discriminative indicate that TimeLDM consistently and significantly outperforms current state-of-the-art benchmarks with an average improvement of 3.4$times$ and 3.8$times$, respectively. Further studies demonstrate that our method presents better performance on different lengths of time series data generation. To the best of our knowledge, this is the first study to explore the potential of the latent diffusion model for unconditional time series generation and establish a new baseline for synthetic time series. | [
"['Jian Qian' 'Miao Sun' 'Sifan Zhou' 'Biao Wan' 'Minhao Li'\n 'Patrick Chiang']"
] |
null | null | 2407.04236 | null | null | http://arxiv.org/pdf/2407.04236v1 | 2024-07-05T03:26:37Z | 2024-07-05T03:26:37Z | Graph Pooling via Ricci Flow | Graph Machine Learning often involves the clustering of nodes based on similarity structure encoded in the graph's topology and the nodes' attributes. On homophilous graphs, the integration of pooling layers has been shown to enhance the performance of Graph Neural Networks by accounting for inherent multi-scale structure. Here, similar nodes are grouped together to coarsen the graph and reduce the input size in subsequent layers in deeper architectures. In both settings, the underlying clustering approach can be implemented via graph pooling operators, which often rely on classical tools from Graph Theory. In this work, we introduce a graph pooling operator (ORC-Pool), which utilizes a characterization of the graph's geometry via Ollivier's discrete Ricci curvature and an associated geometric flow. Previous Ricci flow based clustering approaches have shown great promise across several domains, but are by construction unable to account for similarity structure encoded in the node attributes. However, in many ML applications, such information is vital for downstream tasks. ORC-Pool extends such clustering approaches to attributed graphs, allowing for the integration of geometric coarsening into Graph Neural Networks as a pooling layer. | [
"['Amy Feng' 'Melanie Weber']"
] |
null | null | 2407.04240 | null | null | http://arxiv.org/pdf/2407.04240v1 | 2024-07-05T03:56:40Z | 2024-07-05T03:56:40Z | A Two-Step Minimax Q-learning Algorithm for Two-Player Zero-Sum Markov
Games | An interesting iterative procedure is proposed to solve a two-player zero-sum Markov games. First this problem is expressed as a min-max Markov game. Next, a two-step Q-learning algorithm for solving Markov decision problem (MDP) is suitably modified to solve this Markov game. Under a suitable assumption, the boundedness of the proposed iterates is obtained theoretically. Using results from stochastic approximation, the almost sure convergence of the proposed two-step minimax Q-learning is obtained theoretically. More specifically, the proposed algorithm converges to the game theoretic optimal value with probability one, when the model information is not known. Numerical simulation authenticate that the proposed algorithm is effective and easy to implement. | [
"['Shreyas S R' 'Antony Vijesh']"
] |
null | null | 2407.04248 | null | null | http://arxiv.org/pdf/2407.04248v1 | 2024-07-05T04:30:41Z | 2024-07-05T04:30:41Z | Machine Learning for Complex Systems with Abnormal Pattern by Exception
Maximization Outlier Detection Method | This paper proposes a novel fast online methodology for outlier detection called the exception maximization outlier detection method(EMODM), which employs probabilistic models and statistical algorithms to detect abnormal patterns from the outputs of complex systems. The EMODM is based on a two-state Gaussian mixture model and demonstrates strong performance in probability anomaly detection working on real-time raw data rather than using special prior distribution information. We confirm this using the synthetic data from two numerical cases. For the real-world data, we have detected the short circuit pattern of the circuit system using EMODM by the current and voltage output of a three-phase inverter. The EMODM also found an abnormal period due to COVID-19 in the insured unemployment data of 53 regions in the United States from 2000 to 2024. The application of EMODM to these two real-life datasets demonstrated the effectiveness and accuracy of our algorithm. | [
"['Zhikun Zhang' 'Yiting Duan' 'Xiangjun Wang' 'Mingyuan Zhang']"
] |
null | null | 2407.04251 | null | null | http://arxiv.org/pdf/2407.04251v1 | 2024-07-05T04:38:17Z | 2024-07-05T04:38:17Z | Unified Interpretation of Smoothing Methods for Negative Sampling Loss
Functions in Knowledge Graph Embedding | Knowledge Graphs (KGs) are fundamental resources in knowledge-intensive tasks in NLP. Due to the limitation of manually creating KGs, KG Completion (KGC) has an important role in automatically completing KGs by scoring their links with KG Embedding (KGE). To handle many entities in training, KGE relies on Negative Sampling (NS) loss that can reduce the computational cost by sampling. Since the appearance frequencies for each link are at most one in KGs, sparsity is an essential and inevitable problem. The NS loss is no exception. As a solution, the NS loss in KGE relies on smoothing methods like Self-Adversarial Negative Sampling (SANS) and subsampling. However, it is uncertain what kind of smoothing method is suitable for this purpose due to the lack of theoretical understanding. This paper provides theoretical interpretations of the smoothing methods for the NS loss in KGE and induces a new NS loss, Triplet Adaptive Negative Sampling (TANS), that can cover the characteristics of the conventional smoothing methods. Experimental results of TransE, DistMult, ComplEx, RotatE, HAKE, and HousE on FB15k-237, WN18RR, and YAGO3-10 datasets and their sparser subsets show the soundness of our interpretation and performance improvement by our TANS. | [
"['Xincan Feng' 'Hidetaka Kamigaito' 'Katsuhiko Hayashi' 'Taro Watanabe']"
] |
null | null | 2407.04258 | null | null | http://arxiv.org/pdf/2407.04258v1 | 2024-07-05T05:08:06Z | 2024-07-05T05:08:06Z | Unsupervised Video Summarization via Reinforcement Learning and a
Trained Evaluator | This paper presents a novel approach for unsupervised video summarization using reinforcement learning. It aims to address the existing limitations of current unsupervised methods, including unstable training of adversarial generator-discriminator architectures and reliance on hand-crafted reward functions for quality evaluation. The proposed method is based on the concept that a concise and informative summary should result in a reconstructed video that closely resembles the original. The summarizer model assigns an importance score to each frame and generates a video summary. In the proposed scheme, reinforcement learning, coupled with a unique reward generation pipeline, is employed to train the summarizer model. The reward generation pipeline trains the summarizer to create summaries that lead to improved reconstructions. It comprises a generator model capable of reconstructing masked frames from a partially masked video, along with a reward mechanism that compares the reconstructed video from the summary against the original. The video generator is trained in a self-supervised manner to reconstruct randomly masked frames, enhancing its ability to generate accurate summaries. This training pipeline results in a summarizer model that better mimics human-generated video summaries compared to methods relying on hand-crafted rewards. The training process consists of two stable and isolated training steps, unlike adversarial architectures. Experimental results demonstrate promising performance, with F-scores of 62.3 and 54.5 on TVSum and SumMe datasets, respectively. Additionally, the inference stage is 300 times faster than our previously reported state-of-the-art method. | [
"['Mehryar Abbasi' 'Hadi Hadizadeh' 'Parvaneh Saeedi']"
] |
null | null | 2407.04259 | null | null | http://arxiv.org/pdf/2407.04259v1 | 2024-07-05T05:19:36Z | 2024-07-05T05:19:36Z | Robust Q-Learning for finite ambiguity sets | In this paper we propose a novel $Q$-learning algorithm allowing to solve distributionally robust Markov decision problems for which the ambiguity set of probability measures can be chosen arbitrarily as long as it comprises only a finite amount of measures. Therefore, our approach goes beyond the well-studied cases involving ambiguity sets of balls around some reference measure with the distance to reference measure being measured with respect to the Wasserstein distance or the Kullback--Leibler divergence. Hence, our approach allows the applicant to create ambiguity sets better tailored to her needs and to solve the associated robust Markov decision problem via a $Q$-learning algorithm whose convergence is guaranteed by our main result. Moreover, we showcase in several numerical experiments the tractability of our approach. | [
"['Cécile Decker' 'Julian Sester']"
] |
null | null | 2407.04264 | null | null | http://arxiv.org/pdf/2407.04264v1 | 2024-07-05T05:34:10Z | 2024-07-05T05:34:10Z | Langevin Dynamics: A Unified Perspective on Optimization via Lyapunov
Potentials | We study the problem of non-convex optimization using Stochastic Gradient Langevin Dynamics (SGLD). SGLD is a natural and popular variation of stochastic gradient descent where at each step, appropriately scaled Gaussian noise is added. To our knowledge, the only strategy for showing global convergence of SGLD on the loss function is to show that SGLD can sample from a stationary distribution which assigns larger mass when the function is small (the Gibbs measure), and then to convert these guarantees to optimization results. We employ a new strategy to analyze the convergence of SGLD to global minima, based on Lyapunov potentials and optimization. We convert the same mild conditions from previous works on SGLD into geometric properties based on Lyapunov potentials. This adapts well to the case with a stochastic gradient oracle, which is natural for machine learning applications where one wants to minimize population loss but only has access to stochastic gradients via minibatch training samples. Here we provide 1) improved rates in the setting of previous works studying SGLD for optimization, 2) the first finite gradient complexity guarantee for SGLD where the function is Lipschitz and the Gibbs measure defined by the function satisfies a Poincar'e Inequality, and 3) prove if continuous-time Langevin Dynamics succeeds for optimization, then discrete-time SGLD succeeds under mild regularity assumptions. | [
"['August Y. Chen' 'Ayush Sekhari' 'Karthik Sridharan']"
] |
null | null | 2407.04268 | null | null | http://arxiv.org/pdf/2407.04268v2 | 2024-07-12T17:10:14Z | 2024-07-05T05:45:34Z | NeuFair: Neural Network Fairness Repair with Dropout | This paper investigates neuron dropout as a post-processing bias mitigation for deep neural networks (DNNs). Neural-driven software solutions are increasingly applied in socially critical domains with significant fairness implications. While neural networks are exceptionally good at finding statistical patterns from data, they may encode and amplify existing biases from the historical data. Existing bias mitigation algorithms often require modifying the input dataset or the learning algorithms. We posit that the prevalent dropout methods that prevent over-fitting during training by randomly dropping neurons may be an effective and less intrusive approach to improve the fairness of pre-trained DNNs. However, finding the ideal set of neurons to drop is a combinatorial problem. We propose NeuFair, a family of post-processing randomized algorithms that mitigate unfairness in pre-trained DNNs via dropouts during inference after training. Our randomized search is guided by an objective to minimize discrimination while maintaining the model's utility. We show that our design of randomized algorithms is effective and efficient in improving fairness (up to 69%) with minimal or no model performance degradation. We provide intuitive explanations of these phenomena and carefully examine the influence of various hyperparameters of search algorithms on the results. Finally, we empirically and conceptually compare NeuFair to different state-of-the-art bias mitigators. | [
"['Vishnu Asutosh Dasu' 'Ashish Kumar' 'Saeid Tizpaz-Niari' 'Gang Tan']"
] |
null | null | 2407.04271 | null | null | http://arxiv.org/pdf/2407.04271v1 | 2024-07-05T05:52:51Z | 2024-07-05T05:52:51Z | Variational Partial Group Convolutions for Input-Aware Partial
Equivariance of Rotations and Color-Shifts | Group Equivariant CNNs (G-CNNs) have shown promising efficacy in various tasks, owing to their ability to capture hierarchical features in an equivariant manner. However, their equivariance is fixed to the symmetry of the whole group, limiting adaptability to diverse partial symmetries in real-world datasets, such as limited rotation symmetry of handwritten digit images and limited color-shift symmetry of flower images. Recent efforts address this limitation, one example being Partial G-CNN which restricts the output group space of convolution layers to break full equivariance. However, such an approach still fails to adjust equivariance levels across data. In this paper, we propose a novel approach, Variational Partial G-CNN (VP G-CNN), to capture varying levels of partial equivariance specific to each data instance. VP G-CNN redesigns the distribution of the output group elements to be conditioned on input data, leveraging variational inference to avoid overfitting. This enables the model to adjust its equivariance levels according to the needs of individual data points. Additionally, we address training instability inherent in discrete group equivariance models by redesigning the reparametrizable distribution. We demonstrate the effectiveness of VP G-CNN on both toy and real-world datasets, including MNIST67-180, CIFAR10, ColorMNIST, and Flowers102. Our results show robust performance, even in uncertainty metrics. | [
"['Hyunsu Kim' 'Yegon Kim' 'Hongseok Yang' 'Juho Lee']"
] |
null | null | 2407.04272 | null | null | http://arxiv.org/pdf/2407.04272v3 | 2024-07-11T15:31:53Z | 2024-07-05T05:55:18Z | Accelerating Communication in Deep Learning Recommendation Model
Training with Dual-Level Adaptive Lossy Compression | DLRM is a state-of-the-art recommendation system model that has gained widespread adoption across various industry applications. The large size of DLRM models, however, necessitates the use of multiple devices/GPUs for efficient training. A significant bottleneck in this process is the time-consuming all-to-all communication required to collect embedding data from all devices. To mitigate this, we introduce a method that employs error-bounded lossy compression to reduce the communication data size and accelerate DLRM training. We develop a novel error-bounded lossy compression algorithm, informed by an in-depth analysis of embedding data features, to achieve high compression ratios. Moreover, we introduce a dual-level adaptive strategy for error-bound adjustment, spanning both table-wise and iteration-wise aspects, to balance the compression benefits with the potential impacts on accuracy. We further optimize our compressor for PyTorch tensors on GPUs, minimizing compression overhead. Evaluation shows that our method achieves a 1.38$times$ training speedup with a minimal accuracy impact. | [
"['Hao Feng' 'Boyuan Zhang' 'Fanjiang Ye' 'Min Si' 'Ching-Hsiang Chu'\n 'Jiannan Tian' 'Chunxing Yin' 'Summer Deng' 'Yuchen Hao' 'Pavan Balaji'\n 'Tong Geng' 'Dingwen Tao']"
] |
null | null | 2407.04279 | null | null | http://arxiv.org/pdf/2407.04279v1 | 2024-07-05T06:25:34Z | 2024-07-05T06:25:34Z | BiosERC: Integrating Biography Speakers Supported by LLMs for ERC Tasks | In the Emotion Recognition in Conversation task, recent investigations have utilized attention mechanisms exploring relationships among utterances from intra- and inter-speakers for modeling emotional interaction between them. However, attributes such as speaker personality traits remain unexplored and present challenges in terms of their applicability to other tasks or compatibility with diverse model architectures. Therefore, this work introduces a novel framework named BiosERC, which investigates speaker characteristics in a conversation. By employing Large Language Models (LLMs), we extract the "biographical information" of the speaker within a conversation as supplementary knowledge injected into the model to classify emotional labels for each utterance. Our proposed method achieved state-of-the-art (SOTA) results on three famous benchmark datasets: IEMOCAP, MELD, and EmoryNLP, demonstrating the effectiveness and generalization of our model and showcasing its potential for adaptation to various conversation analysis tasks. Our source code is available at https://github.com/yingjie7/BiosERC. | [
"['Jieying Xue' 'Minh Phuong Nguyen' 'Blake Matheny' 'Le Minh Nguyen']"
] |
null | null | 2407.04285 | null | null | http://arxiv.org/pdf/2407.04285v1 | 2024-07-05T06:34:32Z | 2024-07-05T06:34:32Z | Robust Decision Transformer: Tackling Data Corruption in Offline RL via
Sequence Modeling | Learning policies from offline datasets through offline reinforcement learning (RL) holds promise for scaling data-driven decision-making and avoiding unsafe and costly online interactions. However, real-world data collected from sensors or humans often contains noise and errors, posing a significant challenge for existing offline RL methods. Our study indicates that traditional offline RL methods based on temporal difference learning tend to underperform Decision Transformer (DT) under data corruption, especially when the amount of data is limited. This suggests the potential of sequential modeling for tackling data corruption in offline RL. To further unleash the potential of sequence modeling methods, we propose Robust Decision Transformer (RDT) by incorporating several robust techniques. Specifically, we introduce Gaussian weighted learning and iterative data correction to reduce the effect of corrupted data. Additionally, we leverage embedding dropout to enhance the model's resistance to erroneous inputs. Extensive experiments on MoJoCo, KitChen, and Adroit tasks demonstrate RDT's superior performance under diverse data corruption compared to previous methods. Moreover, RDT exhibits remarkable robustness in a challenging setting that combines training-time data corruption with testing-time observation perturbations. These results highlight the potential of robust sequence modeling for learning from noisy or corrupted offline datasets, thereby promoting the reliable application of offline RL in real-world tasks. | [
"['Jiawei Xu' 'Rui Yang' 'Feng Luo' 'Meng Fang' 'Baoxiang Wang' 'Lei Han']"
] |
null | null | 2407.04291 | null | null | http://arxiv.org/pdf/2407.04291v1 | 2024-07-05T06:54:24Z | 2024-07-05T06:54:24Z | We Need Variations in Speech Synthesis: Sub-center Modelling for Speaker
Embeddings | In speech synthesis, modeling of rich emotions and prosodic variations present in human voice are crucial to synthesize natural speech. Although speaker embeddings have been widely used in personalized speech synthesis as conditioning inputs, they are designed to lose variation to optimize speaker recognition accuracy. Thus, they are suboptimal for speech synthesis in terms of modeling the rich variations at the output speech distribution. In this work, we propose a novel speaker embedding network which utilizes multiple class centers in the speaker classification training rather than a single class center as traditional embeddings. The proposed approach introduces variations in the speaker embedding while retaining the speaker recognition performance since model does not have to map all of the utterances of a speaker into a single class center. We apply our proposed embedding in voice conversion task and show that our method provides better naturalness and prosody in synthesized speech. | [
"['Ismail Rasim Ulgen' 'Carlos Busso' 'John H. L. Hansen' 'Berrak Sisman']"
] |
null | null | 2407.04295 | null | null | http://arxiv.org/pdf/2407.04295v1 | 2024-07-05T06:57:30Z | 2024-07-05T06:57:30Z | Jailbreak Attacks and Defenses Against Large Language Models: A Survey | Large Language Models (LLMs) have performed exceptionally in various text-generative tasks, including question answering, translation, code completion, etc. However, the over-assistance of LLMs has raised the challenge of "jailbreaking", which induces the model to generate malicious responses against the usage policy and society by designing adversarial prompts. With the emergence of jailbreak attack methods exploiting different vulnerabilities in LLMs, the corresponding safety alignment measures are also evolving. In this paper, we propose a comprehensive and detailed taxonomy of jailbreak attack and defense methods. For instance, the attack methods are divided into black-box and white-box attacks based on the transparency of the target model. Meanwhile, we classify defense methods into prompt-level and model-level defenses. Additionally, we further subdivide these attack and defense methods into distinct sub-classes and present a coherent diagram illustrating their relationships. We also conduct an investigation into the current evaluation methods and compare them from different perspectives. Our findings aim to inspire future research and practical implementations in safeguarding LLMs against adversarial attacks. Above all, although jailbreak remains a significant concern within the community, we believe that our work enhances the understanding of this domain and provides a foundation for developing more secure LLMs. | [
"['Sibo Yi' 'Yule Liu' 'Zhen Sun' 'Tianshuo Cong' 'Xinlei He'\n 'Jiaxing Song' 'Ke Xu' 'Qi Li']"
] |
null | null | 2407.04302 | null | null | http://arxiv.org/pdf/2407.04302v2 | 2024-07-12T08:35:33Z | 2024-07-05T07:10:26Z | Fair Federated Data Clustering through Personalization: Bridging the Gap
between Diverse Data Distributions | The rapid growth of data from edge devices has catalyzed the performance of machine learning algorithms. However, the data generated resides at client devices thus there are majorly two challenge faced by traditional machine learning paradigms - centralization of data for training and secondly for most the generated data the class labels are missing and there is very poor incentives to clients to manually label their data owing to high cost and lack of expertise. To overcome these issues, there have been initial attempts to handle unlabelled data in a privacy preserving distributed manner using unsupervised federated data clustering. The goal is partition the data available on clients into $k$ partitions (called clusters) without actual exchange of data. Most of the existing algorithms are highly dependent on data distribution patterns across clients or are computationally expensive. Furthermore, due to presence of skewed nature of data across clients in most of practical scenarios existing models might result in clients suffering high clustering cost making them reluctant to participate in federated process. To this, we are first to introduce the idea of personalization in federated clustering. The goal is achieve balance between achieving lower clustering cost and at same time achieving uniform cost across clients. We propose p-FClus that addresses these goal in a single round of communication between server and clients. We validate the efficacy of p-FClus against variety of federated datasets showcasing it's data independence nature, applicability to any finite $ell$-norm, while simultaneously achieving lower cost and variance. | [
"['Shivam Gupta' 'Tarushi' 'Tsering Wangzes' 'Shweta Jain']"
] |
null | null | 2407.04307 | null | null | http://arxiv.org/pdf/2407.04307v1 | 2024-07-05T07:22:44Z | 2024-07-05T07:22:44Z | Crafting Large Language Models for Enhanced Interpretability | We introduce the Concept Bottleneck Large Language Model (CB-LLM), a pioneering approach to creating inherently interpretable Large Language Models (LLMs). Unlike traditional black-box LLMs that rely on post-hoc interpretation methods with limited neuron function insights, CB-LLM sets a new standard with its built-in interpretability, scalability, and ability to provide clear, accurate explanations. This innovation not only advances transparency in language models but also enhances their effectiveness. Our unique Automatic Concept Correction (ACC) strategy successfully narrows the performance gap with conventional black-box LLMs, positioning CB-LLM as a model that combines the high accuracy of traditional LLMs with the added benefit of clear interpretability -- a feature markedly absent in existing LLMs. | [
"['Chung-En Sun' 'Tuomas Oikarinen' 'Tsui-Wei Weng']"
] |
null | null | 2407.04308 | null | null | http://arxiv.org/pdf/2407.04308v2 | 2024-07-08T02:37:44Z | 2024-07-05T07:23:51Z | SSP-GNN: Learning to Track via Bilevel Optimization | We propose a graph-based tracking formulation for multi-object tracking (MOT) where target detections contain kinematic information and re-identification features (attributes). Our method applies a successive shortest paths (SSP) algorithm to a tracking graph defined over a batch of frames. The edge costs in this tracking graph are computed via a message-passing network, a graph neural network (GNN) variant. The parameters of the GNN, and hence, the tracker, are learned end-to-end on a training set of example ground-truth tracks and detections. Specifically, learning takes the form of bilevel optimization guided by our novel loss function. We evaluate our algorithm on simulated scenarios to understand its sensitivity to scenario aspects and model hyperparameters. Across varied scenario complexities, our method compares favorably to a strong baseline. | [
"['Griffin Golias' 'Masa Nakura-Fan' 'Vitaly Ablavsky']"
] |
null | null | 2407.04325 | null | null | http://arxiv.org/pdf/2407.04325v1 | 2024-07-05T07:53:52Z | 2024-07-05T07:53:52Z | Understanding the Role of Invariance in Transfer Learning | Transfer learning is a powerful technique for knowledge-sharing between different tasks. Recent work has found that the representations of models with certain invariances, such as to adversarial input perturbations, achieve higher performance on downstream tasks. These findings suggest that invariance may be an important property in the context of transfer learning. However, the relationship of invariance with transfer performance is not fully understood yet and a number of questions remain. For instance, how important is invariance compared to other factors of the pretraining task? How transferable is learned invariance? In this work, we systematically investigate the importance of representational invariance for transfer learning, as well as how it interacts with other parameters during pretraining. To do so, we introduce a family of synthetic datasets that allow us to precisely control factors of variation both in training and test data. Using these datasets, we a) show that for learning representations with high transfer performance, invariance to the right transformations is as, or often more, important than most other factors such as the number of training samples, the model architecture and the identity of the pretraining classes, b) show conditions under which invariance can harm the ability to transfer representations and c) explore how transferable invariance is between tasks. The code is available at url{https://github.com/tillspeicher/representation-invariance-transfer}. | [
"['Till Speicher' 'Vedant Nanda' 'Krishna P. Gummadi']"
] |
null | null | 2407.04328 | null | null | http://arxiv.org/pdf/2407.04328v1 | 2024-07-05T08:01:19Z | 2024-07-05T08:01:19Z | EAGERx: Graph-Based Framework for Sim2real Robot Learning | Sim2real, that is, the transfer of learned control policies from simulation to real world, is an area of growing interest in robotics due to its potential to efficiently handle complex tasks. The sim2real approach faces challenges due to mismatches between simulation and reality. These discrepancies arise from inaccuracies in modeling physical phenomena and asynchronous control, among other factors. To this end, we introduce EAGERx, a framework with a unified software pipeline for both real and simulated robot learning. It can support various simulators and aids in integrating state, action and time-scale abstractions to facilitate learning. EAGERx's integrated delay simulation, domain randomization features, and proposed synchronization algorithm contribute to narrowing the sim2real gap. We demonstrate (in the context of robot learning and beyond) the efficacy of EAGERx in accommodating diverse robotic systems and maintaining consistent simulation behavior. EAGERx is open source and its code is available at https://eagerx.readthedocs.io. | [
"['Bas van der Heijden' 'Jelle Luijkx' 'Laura Ferranti' 'Jens Kober'\n 'Robert Babuska']"
] |
null | null | 2407.04334 | null | null | http://arxiv.org/pdf/2407.04334v1 | 2024-07-05T08:19:36Z | 2024-07-05T08:19:36Z | Learning Geometric Invariant Features for Classification of Vector
Polygons with Graph Message-passing Neural Network | Geometric shape classification of vector polygons remains a non-trivial learning task in spatial analysis. Previous studies mainly focus on devising deep learning approaches for representation learning of rasterized vector polygons, whereas the study of discrete representations of polygons and subsequent deep learning approaches have not been fully investigated. In this study, we investigate a graph representation of vector polygons and propose a novel graph message-passing neural network (PolyMP) to learn the geometric-invariant features for shape classification of polygons. Through extensive experiments, we show that the graph representation of polygons combined with a permutation-invariant graph message-passing neural network achieves highly robust performances on benchmark datasets (i.e., synthetic glyph and real-world building footprint datasets) as compared to baseline methods. We demonstrate that the proposed graph-based PolyMP network enables the learning of expressive geometric features invariant to geometric transformations of polygons (i.e., translation, rotation, scaling and shearing) and is robust to trivial vertex removals of polygons. We further show the strong generalizability of PolyMP, which enables generalizing the learned geometric features from the synthetic glyph polygons to the real-world building footprints. | [
"['Zexian Huang' 'Kourosh Khoshelham' 'Martin Tomko']"
] |
null | null | 2407.04335 | null | null | http://arxiv.org/pdf/2407.04335v1 | 2024-07-05T08:20:27Z | 2024-07-05T08:20:27Z | Geometrically Inspired Kernel Machines for Collaborative Learning Beyond
Gradient Descent | This paper develops a novel mathematical framework for collaborative learning by means of geometrically inspired kernel machines which includes statements on the bounds of generalisation and approximation errors, and sample complexity. For classification problems, this approach allows us to learn bounded geometric structures around given data points and hence solve the global model learning problem in an efficient way by exploiting convexity properties of the related optimisation problem in a Reproducing Kernel Hilbert Space (RKHS). In this way, we can reduce classification problems to determining the closest bounded geometric structure from a given data point. Further advantages that come with our solution is that our approach does not require clients to perform multiple epochs of local optimisation using stochastic gradient descent, nor require rounds of communication between client/server for optimising the global model. We highlight that numerous experiments have shown that the proposed method is a competitive alternative to the state-of-the-art. | [
"['Mohit Kumar' 'Alexander Valentinitsch' 'Magdalena Fuchs'\n 'Mathias Brucker' 'Juliana Bowles' 'Adnan Husakovic' 'Ali Abbas'\n 'Bernhard A. Moser']"
] |
null | null | 2407.04343 | null | null | http://arxiv.org/pdf/2407.04343v1 | 2024-07-05T08:34:49Z | 2024-07-05T08:34:49Z | Enhancing Safety for Autonomous Agents in Partly Concealed Urban Traffic
Environments Through Representation-Based Shielding | Navigating unsignalized intersections in urban environments poses a complex challenge for self-driving vehicles, where issues such as view obstructions, unpredictable pedestrian crossings, and diverse traffic participants demand a great focus on crash prevention. In this paper, we propose a novel state representation for Reinforcement Learning (RL) agents centered around the information perceivable by an autonomous agent, enabling the safe navigation of previously uncharted road maps. Our approach surpasses several baseline models by a sig nificant margin in terms of safety and energy consumption metrics. These improvements are achieved while maintaining a competitive average travel speed. Our findings pave the way for more robust and reliable autonomous navigation strategies, promising safer and more efficient urban traffic environments. | [
"['Pierre Haritz' 'David Wanke' 'Thomas Liebig']"
] |
null | null | 2407.04352 | null | null | http://arxiv.org/pdf/2407.04352v1 | 2024-07-05T08:46:16Z | 2024-07-05T08:46:16Z | UpStory: the Uppsala Storytelling dataset | Friendship and rapport play an important role in the formation of constructive social interactions, and have been widely studied in educational settings due to their impact on student outcomes. Given the growing interest in automating the analysis of such phenomena through Machine Learning (ML), access to annotated interaction datasets is highly valuable. However, no dataset on dyadic child-child interactions explicitly capturing rapport currently exists. Moreover, despite advances in the automatic analysis of human behaviour, no previous work has addressed the prediction of rapport in child-child dyadic interactions in educational settings. We present UpStory -- the Uppsala Storytelling dataset: a novel dataset of naturalistic dyadic interactions between primary school aged children, with an experimental manipulation of rapport. Pairs of children aged 8-10 participate in a task-oriented activity: designing a story together, while being allowed free movement within the play area. We promote balanced collection of different levels of rapport by using a within-subjects design: self-reported friendships are used to pair each child twice, either minimizing or maximizing pair separation in the friendship network. The dataset contains data for 35 pairs, totalling 3h 40m of audio and video recordings. It includes two video sources covering the play area, as well as separate voice recordings for each child. An anonymized version of the dataset is made publicly available, containing per-frame head pose, body pose, and face features; as well as per-pair information, including the level of rapport. Finally, we provide ML baselines for the prediction of rapport. | [
"['Marc Fraile' 'Natalia Calvo-Barajas' 'Anastasia Sophia Apeiron'\n 'Giovanna Varni' 'Joakim Lindblad' 'Nataša Sladoje' 'Ginevra Castellano']"
] |
null | null | 2407.04358 | null | null | http://arxiv.org/pdf/2407.04358v1 | 2024-07-05T08:53:06Z | 2024-07-05T08:53:06Z | An Adaptive Stochastic Gradient Method with Non-negative Gauss-Newton
Stepsizes | We consider the problem of minimizing the average of a large number of smooth but possibly non-convex functions. In the context of most machine learning applications, each loss function is non-negative and thus can be expressed as the composition of a square and its real-valued square root. This reformulation allows us to apply the Gauss-Newton method, or the Levenberg-Marquardt method when adding a quadratic regularization. The resulting algorithm, while being computationally as efficient as the vanilla stochastic gradient method, is highly adaptive and can automatically warmup and decay the effective stepsize while tracking the non-negative loss landscape. We provide a tight convergence analysis, leveraging new techniques, in the stochastic convex and non-convex settings. In particular, in the convex case, the method does not require access to the gradient Lipshitz constant for convergence, and is guaranteed to never diverge. The convergence rates and empirical evaluations compare favorably to the classical (stochastic) gradient method as well as to several other adaptive methods. | [
"['Antonio Orvieto' 'Lin Xiao']"
] |
null | null | 2407.04370 | null | null | http://arxiv.org/pdf/2407.04370v2 | 2024-07-09T03:09:41Z | 2024-07-05T09:16:56Z | Regulating Model Reliance on Non-Robust Features by Smoothing Input
Marginal Density | Trustworthy machine learning necessitates meticulous regulation of model reliance on non-robust features. We propose a framework to delineate and regulate such features by attributing model predictions to the input. Within our approach, robust feature attributions exhibit a certain consistency, while non-robust feature attributions are susceptible to fluctuations. This behavior allows identification of correlation between model reliance on non-robust features and smoothness of marginal density of the input samples. Hence, we uniquely regularize the gradients of the marginal density w.r.t. the input features for robustness. We also devise an efficient implementation of our regularization to address the potential numerical instability of the underlying optimization process. Moreover, we analytically reveal that, as opposed to our marginal density smoothing, the prevalent input gradient regularization smoothens conditional or joint density of the input, which can cause limited robustness. Our experiments validate the effectiveness of the proposed method, providing clear evidence of its capability to address the feature leakage problem and mitigate spurious correlations. Extensive results further establish that our technique enables the model to exhibit robustness against perturbations in pixel values, input gradients, and density. | [
"['Peiyu Yang' 'Naveed Akhtar' 'Mubarak Shah' 'Ajmal Mian']"
] |
null | null | 2407.04393 | null | null | http://arxiv.org/pdf/2407.04393v1 | 2024-07-05T10:05:35Z | 2024-07-05T10:05:35Z | Function Smoothing Regularization for Precision Factorization Machine
Annealing in Continuous Variable Optimization Problems | Solving continuous variable optimization problems by factorization machine quantum annealing (FMQA) demonstrates the potential of Ising machines to be extended as a solver for integer and real optimization problems. However, the details of the Hamiltonian function surface obtained by factorization machine (FM) have been overlooked. This study shows that in the widely common case where real numbers are represented by a combination of binary variables, the function surface of the Hamiltonian obtained by FM can be very noisy. This noise interferes with the inherent capabilities of quantum annealing and is likely to be a substantial cause of problems previously considered unsolvable due to the limitations of FMQA performance. The origin of the noise is identified and a simple, general method is proposed to prevent its occurrence. The generalization performance of the proposed method and its ability to solve practical problems is demonstrated. | [
"['Katsuhiro Endo' 'Kazuaki Z. Takahashi']"
] |
null | null | 2407.04400 | null | null | http://arxiv.org/pdf/2407.04400v1 | 2024-07-05T10:20:24Z | 2024-07-05T10:20:24Z | Hard-Attention Gates with Gradient Routing for Endoscopic Image
Computing | To address overfitting and enhance model generalization in gastroenterological polyp size assessment, our study introduces Feature-Selection Gates (FSG) or Hard-Attention Gates (HAG) alongside Gradient Routing (GR) for dynamic feature selection. This technique aims to boost Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) by promoting sparse connectivity, thereby reducing overfitting and enhancing generalization. HAG achieves this through sparsification with learnable weights, serving as a regularization strategy. GR further refines this process by optimizing HAG parameters via dual forward passes, independently from the main model, to improve feature re-weighting. Our evaluation spanned multiple datasets, including CIFAR-100 for a broad impact assessment and specialized endoscopic datasets (REAL-Colon, Misawa, and SUN) focusing on polyp size estimation, covering over 200 polyps in more than 370,000 frames. The findings indicate that our HAG-enhanced networks substantially enhance performance in both binary and triclass classification tasks related to polyp sizing. Specifically, CNNs experienced an F1 Score improvement to 87.8% in binary classification, while in triclass classification, the ViT-T model reached an F1 Score of 76.5%, outperforming traditional CNNs and ViT-T models. To facilitate further research, we are releasing our codebase, which includes implementations for CNNs, multistream CNNs, ViT, and HAG-augmented variants. This resource aims to standardize the use of endoscopic datasets, providing public training-validation-testing splits for reliable and comparable research in gastroenterological polyp size estimation. The codebase is available at github.com/cosmoimd/feature-selection-gates. | [
"['Giorgio Roffo' 'Carlo Biffi' 'Pietro Salvagnini' 'Andrea Cherubini']"
] |
null | null | 2407.04405 | null | null | http://arxiv.org/pdf/2407.04405v1 | 2024-07-05T10:41:15Z | 2024-07-05T10:41:15Z | Discovering symbolic expressions with parallelized tree search | Symbolic regression plays a crucial role in modern scientific research thanks to its capability of discovering concise and interpretable mathematical expressions from data. A grand challenge lies in the arduous search for parsimonious and generalizable mathematical formulas, in an infinite search space, while intending to fit the training data. Existing algorithms have faced a critical bottleneck of accuracy and efficiency over a decade when handling problems of complexity, which essentially hinders the pace of applying symbolic regression for scientific exploration across interdisciplinary domains. To this end, we introduce a parallelized tree search (PTS) model to efficiently distill generic mathematical expressions from limited data. Through a series of extensive experiments, we demonstrate the superior accuracy and efficiency of PTS for equation discovery, which greatly outperforms the state-of-the-art baseline models on over 80 synthetic and experimental datasets (e.g., lifting its performance by up to 99% accuracy improvement and one-order of magnitude speed up). PTS represents a key advance in accurate and efficient data-driven discovery of symbolic, interpretable models (e.g., underlying physical laws) and marks a pivotal transition towards scalable symbolic learning. | [
"['Kai Ruan' 'Ze-Feng Gao' 'Yike Guo' 'Hao Sun' 'Ji-Rong Wen' 'Yang Liu']"
] |
null | null | 2407.04406 | null | null | http://arxiv.org/pdf/2407.04406v1 | 2024-07-05T10:43:24Z | 2024-07-05T10:43:24Z | On Quantum Channel Learning | The problem of an optimal mapping between Hilbert spaces $IN$ and $OUT$, based on a series of density matrix mapping measurements $rho^{(l)} to varrho^{(l)}$, $l=1dots M$, is formulated as an optimization problem maximizing the total fidelity $mathcal{F}=sum_{l=1}^{M} omega^{(l)} Fleft(varrho^{(l)},sum_s B_s rho^{(l)} B^{dagger}_sright)$ subject to probability preservation constraints on Kraus operators $B_s$. For $F(varrho,sigma)$ in the form that total fidelity can be represented as a quadratic form with superoperator $mathcal{F}=sum_sleftlangle B_smiddle|Smiddle| B_s rightrangle$ (either exactly or as an approximation) an iterative algorithm is developed to find the global maximum. The result comprises in $N_s$ operators $B_s$ that collectively form an $IN$ to $OUT$ quantum channel $A^{OUT}=sum_s B_s A^{IN} B_s^{dagger}$. The work introduces two important generalizations of unitary learning: 1. $IN$/$OUT$ states are represented as density matrices. 2. The mapping itself is formulated as a general quantum channel. This marks a crucial advancement from the commonly studied unitary mapping of pure states $phi_l=mathcal{U} psi_l$ to a general quantum channel, what allows us to distinguish probabilistic mixture of states and their superposition. An application of the approach is demonstrated on unitary learning of density matrix mapping $varrho^{(l)}=mathcal{U} rho^{(l)} mathcal{U}^{dagger}$, in this case a quadratic on $mathcal{U}$ fidelity can be constructed by considering $sqrt{rho^{(l)}} to sqrt{varrho^{(l)}}$ mapping, and on a general quantum channel of Kraus rank $N_s$, where quadratic on $B_s$ fidelity is an approximation -- a quantum channel is then built as a hierarchy of unitary mappings. The approach can be applied to study decoherence effects, spontaneous coherence, synchronizing, etc. | [
"['Mikhail Gennadievich Belov' 'Victor Victorovich Dubov'\n 'Alexey Vladimirovich Filimonov' 'Vladislav Gennadievich Malyshkin']"
] |
null | null | 2407.04407 | null | null | http://arxiv.org/pdf/2407.04407v1 | 2024-07-05T10:43:41Z | 2024-07-05T10:43:41Z | Trustworthy Classification through Rank-Based Conformal Prediction Sets | Machine learning classification tasks often benefit from predicting a set of possible labels with confidence scores to capture uncertainty. However, existing methods struggle with the high-dimensional nature of the data and the lack of well-calibrated probabilities from modern classification models. We propose a novel conformal prediction method that employs a rank-based score function suitable for classification models that predict the order of labels correctly, even if not well-calibrated. Our approach constructs prediction sets that achieve the desired coverage rate while managing their size. We provide a theoretical analysis of the expected size of the conformal prediction sets based on the rank distribution of the underlying classifier. Through extensive experiments, we demonstrate that our method outperforms existing techniques on various datasets, providing reliable uncertainty quantification. Our contributions include a novel conformal prediction method, theoretical analysis, and empirical evaluation. This work advances the practical deployment of machine learning systems by enabling reliable uncertainty quantification. | [
"['Rui Luo' 'Zhixin Zhou']"
] |
null | null | 2407.04418 | null | null | http://arxiv.org/pdf/2407.04418v1 | 2024-07-05T11:09:05Z | 2024-07-05T11:09:05Z | Enabling On-Device LLMs Personalization with Smartphone Sensing | This demo presents a novel end-to-end framework that combines on-device large language models (LLMs) with smartphone sensing technologies to achieve context-aware and personalized services. The framework addresses critical limitations of current personalization solutions via cloud-based LLMs, such as privacy concerns, latency and cost, and limited personal sensor data. To achieve this, we innovatively proposed deploying LLMs on smartphones with multimodal sensor data and customized prompt engineering, ensuring privacy and enhancing personalization performance through context-aware sensing. A case study involving a university student demonstrated the proposed framework's capability to provide tailored recommendations. In addition, we show that the proposed framework achieves the best trade-off in privacy, performance, latency, cost, battery and energy consumption between on-device and cloud LLMs. Future work aims to integrate more diverse sensor data and conduct large-scale user studies to further refine the personalization. We envision the proposed framework could significantly improve user experiences in various domains such as healthcare, productivity, and entertainment by providing secure, context-aware, and efficient interactions directly on users' devices. | [
"['Shiquan Zhang' 'Ying Ma' 'Le Fang' 'Hong Jia' \"Simon D'Alfonso\"\n 'Vassilis Kostakos']"
] |
null | null | 2407.04440 | null | null | http://arxiv.org/pdf/2407.04440v1 | 2024-07-05T11:42:39Z | 2024-07-05T11:42:39Z | Wavelet-based Temporal Attention Improves Traffic Forecasting | Spatio-temporal forecasting of traffic flow data represents a typical problem in the field of machine learning, impacting urban traffic management systems. Traditional statistical and machine learning methods cannot adequately handle both the temporal and spatial dependencies in these complex traffic flow datasets. A prevalent approach in the field is to combine graph convolutional networks and multi-head attention mechanisms for spatio-temporal processing. This paper proposes a wavelet-based temporal attention model, namely a wavelet-based dynamic spatio-temporal aware graph neural network (W-DSTAGNN), for tackling the traffic forecasting problem. Benchmark experiments using several statistical metrics confirm that our proposal efficiently captures spatio-temporal correlations and outperforms ten state-of-the-art models on three different real-world traffic datasets. Our proposed ensemble data-driven method can handle dynamic temporal and spatial dependencies and make long-term forecasts in an efficient manner. | [
"['Yash Jakhmola' 'Nitish Kumar Mishra' 'Kripabandhu Ghosh'\n 'Tanujit Chakraborty']"
] |
null | null | 2407.04449 | null | null | http://arxiv.org/pdf/2407.04449v1 | 2024-07-05T12:04:12Z | 2024-07-05T12:04:12Z | Multi-modal Masked Siamese Network Improves Chest X-Ray Representation
Learning | Self-supervised learning methods for medical images primarily rely on the imaging modality during pretraining. While such approaches deliver promising results, they do not leverage associated patient or scan information collected within Electronic Health Records (EHR). Here, we propose to incorporate EHR data during self-supervised pretraining with a Masked Siamese Network (MSN) to enhance the quality of chest X-ray representations. We investigate three types of EHR data, including demographic, scan metadata, and inpatient stay information. We evaluate our approach on three publicly available chest X-ray datasets, MIMIC-CXR, CheXpert, and NIH-14, using two vision transformer (ViT) backbones, specifically ViT-Tiny and ViT-Small. In assessing the quality of the representations via linear evaluation, our proposed method demonstrates significant improvement compared to vanilla MSN and state-of-the-art self-supervised learning baselines. Our work highlights the potential of EHR-enhanced self-supervised pre-training for medical imaging. The code is publicly available at: https://github.com/nyuad-cai/CXR-EHR-MSN | [
"['Saeed Shurrab' 'Alejandro Guerra-Manzanares' 'Farah E. Shamout']"
] |
null | null | 2407.04451 | null | null | http://arxiv.org/pdf/2407.04451v1 | 2024-07-05T12:05:37Z | 2024-07-05T12:05:37Z | Hindsight Preference Learning for Offline Preference-based Reinforcement
Learning | Offline preference-based reinforcement learning (RL), which focuses on optimizing policies using human preferences between pairs of trajectory segments selected from an offline dataset, has emerged as a practical avenue for RL applications. Existing works rely on extracting step-wise reward signals from trajectory-wise preference annotations, assuming that preferences correlate with the cumulative Markovian rewards. However, such methods fail to capture the holistic perspective of data annotation: Humans often assess the desirability of a sequence of actions by considering the overall outcome rather than the immediate rewards. To address this challenge, we propose to model human preferences using rewards conditioned on future outcomes of the trajectory segments, i.e. the hindsight information. For downstream RL optimization, the reward of each step is calculated by marginalizing over possible future outcomes, the distribution of which is approximated by a variational auto-encoder trained using the offline dataset. Our proposed method, Hindsight Preference Learning (HPL), can facilitate credit assignment by taking full advantage of vast trajectory data available in massive unlabeled datasets. Comprehensive empirical studies demonstrate the benefits of HPL in delivering robust and advantageous rewards across various domains. Our code is publicly released at https://github.com/typoverflow/WiseRL. | [
"['Chen-Xiao Gao' 'Shengjun Fang' 'Chenjun Xiao' 'Yang Yu'\n 'Zongzhang Zhang']"
] |
null | null | 2407.04460 | null | null | http://arxiv.org/pdf/2407.04460v1 | 2024-07-05T12:10:54Z | 2024-07-05T12:10:54Z | Smart Sampling: Helping from Friendly Neighbors for Decentralized
Federated Learning | Federated Learning (FL) is gaining widespread interest for its ability to share knowledge while preserving privacy and reducing communication costs. Unlike Centralized FL, Decentralized FL (DFL) employs a network architecture that eliminates the need for a central server, allowing direct communication among clients and leading to significant communication resource savings. However, due to data heterogeneity, not all neighboring nodes contribute to enhancing the local client's model performance. In this work, we introduce textbf{emph{AFIND+}}, a simple yet efficient algorithm for sampling and aggregating neighbors in DFL, with the aim of leveraging collaboration to improve clients' model performance. AFIND+ identifies helpful neighbors, adaptively adjusts the number of selected neighbors, and strategically aggregates the sampled neighbors' models based on their contributions. Numerical results on real-world datasets with diverse data partitions demonstrate that AFIND+ outperforms other sampling algorithms in DFL and is compatible with most existing DFL optimization algorithms. | [
"['Lin Wang' 'Yang Chen' 'Yongxin Guo' 'Xiaoying Tang']"
] |
null | null | 2407.04472 | null | null | http://arxiv.org/pdf/2407.04472v3 | 2024-07-09T13:31:00Z | 2024-07-05T12:42:31Z | EventChat: Implementation and user-centric evaluation of a large
language model-driven conversational recommender system for exploring leisure
events in an SME context | Large language models (LLMs) present an enormous evolution in the strategic potential of conversational recommender systems (CRS). Yet to date, research has predominantly focused upon technical frameworks to implement LLM-driven CRS, rather than end-user evaluations or strategic implications for firms, particularly from the perspective of a small to medium enterprises (SME) that makeup the bedrock of the global economy. In the current paper, we detail the design of an LLM-driven CRS in an SME setting, and its subsequent performance in the field using both objective system metrics and subjective user evaluations. While doing so, we additionally outline a short-form revised ResQue model for evaluating LLM-driven CRS, enabling replicability in a rapidly evolving field. Our results reveal good system performance from a user experience perspective (85.5% recommendation accuracy) but underscore latency, cost, and quality issues challenging business viability. Notably, with a median cost of $0.04 per interaction and a latency of 5.7s, cost-effectiveness and response time emerge as crucial areas for achieving a more user-friendly and economically viable LLM-driven CRS for SME settings. One major driver of these costs is the use of an advanced LLM as a ranker within the retrieval-augmented generation (RAG) technique. Our results additionally indicate that relying solely on approaches such as Prompt-based learning with ChatGPT as the underlying LLM makes it challenging to achieve satisfying quality in a production environment. Strategic considerations for SMEs deploying an LLM-driven CRS are outlined, particularly considering trade-offs in the current technical landscape. | [
"['Hannes Kunstmann' 'Joseph Ollier' 'Joel Persson'\n 'Florian von Wangenheim']"
] |
null | null | 2407.04476 | null | null | http://arxiv.org/pdf/2407.04476v1 | 2024-07-05T12:53:34Z | 2024-07-05T12:53:34Z | Rethinking Data Input for Point Cloud Upsampling | In recent years, point cloud upsampling has been widely applied in fields such as 3D reconstruction and surface generation. However, existing point cloud upsampling inputs are all patch based, and there is no research discussing the differences and principles between point cloud model full input and patch based input. In order to compare with patch based point cloud input, this article proposes a new data input method, which divides the full point cloud model to ensure shape integrity while training PU-GCN. This article was validated on the PU1K and ABC datasets, but the results showed that Patch based performance is better than model based full input i.e. Average Segment input. Therefore, this article explores the data input factors and model modules that affect the upsampling results of point clouds. | [
"['Tongxu Zhang']"
] |
null | null | 2407.04480 | null | null | http://arxiv.org/pdf/2407.04480v1 | 2024-07-05T13:01:36Z | 2024-07-05T13:01:36Z | LoCo: Low-Bit Communication Adaptor for Large-scale Model Training | To efficiently train large-scale models, low-bit gradient communication compresses full-precision gradients on local GPU nodes into low-precision ones for higher gradient synchronization efficiency among GPU nodes. However, it often degrades training quality due to compression information loss. To address this, we propose the Low-bit Communication Adaptor (LoCo), which compensates gradients on local GPU nodes before compression, ensuring efficient synchronization without compromising training quality. Specifically, LoCo designs a moving average of historical compensation errors to stably estimate concurrent compression error and then adopts it to compensate for the concurrent gradient compression, yielding a less lossless compression. This mechanism allows it to be compatible with general optimizers like Adam and sharding strategies like FSDP. Theoretical analysis shows that integrating LoCo into full-precision optimizers like Adam and SGD does not impair their convergence speed on nonconvex problems. Experimental results show that across large-scale model training frameworks like Megatron-LM and PyTorch's FSDP, LoCo significantly improves communication efficiency, e.g., improving Adam's training speed by 14% to 40% without performance degradation on large language models like LLAMAs and MoE. | [
"['Xingyu Xie' 'Zhijie Lin' 'Kim-Chuan Toh' 'Pan Zhou']"
] |
null | null | 2407.04481 | null | null | http://arxiv.org/pdf/2407.04481v1 | 2024-07-05T13:04:06Z | 2024-07-05T13:04:06Z | Using Petri Nets as an Integrated Constraint Mechanism for Reinforcement
Learning Tasks | The lack of trust in algorithms is usually an issue when using Reinforcement Learning (RL) agents for control in real-world domains such as production plants, autonomous vehicles, or traffic-related infrastructure, partly due to the lack of verifiability of the model itself. In such scenarios, Petri nets (PNs) are often available for flowcharts or process steps, as they are versatile and standardized. In order to facilitate integration of RL models and as a step towards increasing AI trustworthiness, we propose an approach that uses PNs with three main advantages over typical RL approaches: Firstly, the agent can now easily be modeled with a combined state including both external environmental observations and agent-specific state information from a given PN. Secondly, we can enforce constraints for state-dependent actions through the inherent PN model. And lastly, we can increase trustworthiness by verifying PN properties through techniques such as model checking. We test our approach on a typical four-way intersection traffic light control setting and present our results, beating cycle-based baselines. | [
"['Timon Sachweh' 'Pierre Haritz' 'Thomas Liebig']"
] |
null | null | 2407.04485 | null | null | http://arxiv.org/pdf/2407.04485v1 | 2024-07-05T13:08:58Z | 2024-07-05T13:08:58Z | Leveraging Graph Structures to Detect Hallucinations in Large Language
Models | Large language models are extensively applied across a wide range of tasks, such as customer support, content creation, educational tutoring, and providing financial guidance. However, a well-known drawback is their predisposition to generate hallucinations. This damages the trustworthiness of the information these models provide, impacting decision-making and user confidence. We propose a method to detect hallucinations by looking at the structure of the latent space and finding associations within hallucinated and non-hallucinated generations. We create a graph structure that connects generations that lie closely in the embedding space. Moreover, we employ a Graph Attention Network which utilizes message passing to aggregate information from neighboring nodes and assigns varying degrees of importance to each neighbor based on their relevance. Our findings show that 1) there exists a structure in the latent space that differentiates between hallucinated and non-hallucinated generations, 2) Graph Attention Networks can learn this structure and generalize it to unseen generations, and 3) the robustness of our method is enhanced when incorporating contrastive learning. When evaluated against evidence-based benchmarks, our model performs similarly without access to search-based methods. | [
"['Noa Nonkes' 'Sergei Agaronian' 'Evangelos Kanoulas' 'Roxana Petcu']"
] |
null | null | 2407.04491 | null | null | http://arxiv.org/pdf/2407.04491v1 | 2024-07-05T13:29:30Z | 2024-07-05T13:29:30Z | Better by Default: Strong Pre-Tuned MLPs and Boosted Trees on Tabular
Data | For classification and regression on tabular data, the dominance of gradient-boosted decision trees (GBDTs) has recently been challenged by often much slower deep learning methods with extensive hyperparameter tuning. We address this discrepancy by introducing (a) RealMLP, an improved multilayer perceptron (MLP), and (b) improved default parameters for GBDTs and RealMLP. We tune RealMLP and the default parameters on a meta-train benchmark with 71 classification and 47 regression datasets and compare them to hyperparameter-optimized versions on a disjoint meta-test benchmark with 48 classification and 42 regression datasets, as well as the GBDT-friendly benchmark by Grinsztajn et al. (2022). Our benchmark results show that RealMLP offers a better time-accuracy tradeoff than other neural nets and is competitive with GBDTs. Moreover, a combination of RealMLP and GBDTs with improved default parameters can achieve excellent results on medium-sized tabular datasets (1K--500K samples) without hyperparameter tuning. | [
"['David Holzmüller' 'Léo Grinsztajn' 'Ingo Steinwart']"
] |
null | null | 2407.04493 | null | null | http://arxiv.org/abs/2407.04493v1 | 2024-07-05T13:32:06Z | 2024-07-05T13:32:06Z | PROUD: PaRetO-gUided Diffusion Model for Multi-objective Generation | Recent advancements in the realm of deep generative models focus on generating samples that satisfy multiple desired properties. However, prevalent approaches optimize these property functions independently, thus omitting the trade-offs among them. In addition, the property optimization is often improperly integrated into the generative models, resulting in an unnecessary compromise on generation quality (i.e., the quality of generated samples). To address these issues, we formulate a constrained optimization problem. It seeks to optimize generation quality while ensuring that generated samples reside at the Pareto front of multiple property objectives. Such a formulation enables the generation of samples that cannot be further improved simultaneously on the conflicting property functions and preserves good quality of generated samples. Building upon this formulation, we introduce the PaRetO-gUided Diffusion model (PROUD), wherein the gradients in the denoising process are dynamically adjusted to enhance generation quality while the generated samples adhere to Pareto optimality. Experimental evaluations on image generation and protein generation tasks demonstrate that our PROUD consistently maintains superior generation quality while approaching Pareto optimality across multiple property functions compared to various baselines. | [
"['Yinghua Yao' 'Yuangang Pan' 'Jing Li' 'Ivor Tsang' 'Xin Yao']"
] |
null | null | 2407.04495 | null | null | http://arxiv.org/pdf/2407.04495v2 | 2024-07-08T02:48:15Z | 2024-07-05T13:35:14Z | Speed-accuracy trade-off for the diffusion models: Wisdom from
nonequilibrium thermodynamics and optimal transport | We discuss a connection between a generative model, called the diffusion model, and nonequilibrium thermodynamics for the Fokker-Planck equation, called stochastic thermodynamics. Based on the techniques of stochastic thermodynamics, we derive the speed-accuracy trade-off for the diffusion models, which is a trade-off relationship between the speed and accuracy of data generation in diffusion models. Our result implies that the entropy production rate in the forward process affects the errors in data generation. From a stochastic thermodynamic perspective, our results provide quantitative insight into how best to generate data in diffusion models. The optimal learning protocol is introduced by the conservative force in stochastic thermodynamics and the geodesic of space by the 2-Wasserstein distance in optimal transport theory. We numerically illustrate the validity of the speed-accuracy trade-off for the diffusion models with different noise schedules such as the cosine schedule, the conditional optimal transport, and the optimal transport. | [
"['Kotaro Ikeda' 'Tomoya Uda' 'Daisuke Okanohara' 'Sosuke Ito']"
] |
null | null | 2407.04507 | null | null | http://arxiv.org/pdf/2407.04507v1 | 2024-07-05T13:46:11Z | 2024-07-05T13:46:11Z | Few-Shot Airway-Tree Modeling using Data-Driven Sparse Priors | The lack of large annotated datasets in medical imaging is an intrinsic burden for supervised Deep Learning (DL) segmentation models. Few-shot learning approaches are cost-effective solutions to transfer pre-trained models using only limited annotated data. However, such methods can be prone to overfitting due to limited data diversity especially when segmenting complex, diverse, and sparse tubular structures like airways. Furthermore, crafting informative image representations has played a crucial role in medical imaging, enabling discriminative enhancement of anatomical details. In this paper, we initially train a data-driven sparsification module to enhance airways efficiently in lung CT scans. We then incorporate these sparse representations in a standard supervised segmentation pipeline as a pretraining step to enhance the performance of the DL models. Results presented on the ATM public challenge cohort show the effectiveness of using sparse priors in pre-training, leading to segmentation Dice score increase by 1% to 10% in full-scale and few-shot learning scenarios, respectively. | [
"['Ali Keshavarzi' 'Elsa Angelini']"
] |
null | null | 2407.04513 | null | null | http://arxiv.org/pdf/2407.04513v1 | 2024-07-05T13:54:15Z | 2024-07-05T13:54:15Z | LayerShuffle: Enhancing Robustness in Vision Transformers by Randomizing
Layer Execution Order | Due to their architecture and how they are trained, artificial neural networks are typically not robust toward pruning, replacing, or shuffling layers at test time. However, such properties would be desirable for different applications, such as distributed neural network architectures where the order of execution cannot be guaranteed or parts of the network can fail during inference. In this work, we address these issues through a number of proposed training approaches for vision transformers whose most important component is randomizing the execution order of attention modules at training time. We show that with our proposed approaches, vision transformers are indeed capable to adapt to arbitrary layer execution orders at test time assuming one tolerates a reduction (about 20%) in accuracy at the same model size. We also find that our trained models can be randomly merged with each other resulting in functional ("Frankenstein") models without loss of performance compared to the source models. Finally, we layer-prune our models at test time and find that their performance declines gracefully. | [
"['Matthias Freiberger' 'Peter Kun' 'Anders Sundnes Løvlie'\n 'Sebastian Risi']"
] |
null | null | 2407.04516 | null | null | http://arxiv.org/pdf/2407.04516v1 | 2024-07-05T13:57:35Z | 2024-07-05T13:57:35Z | G-Adaptive mesh refinement -- leveraging graph neural networks and
differentiable finite element solvers | We present a novel, and effective, approach to the long-standing problem of mesh adaptivity in finite element methods (FEM). FE solvers are powerful tools for solving partial differential equations (PDEs), but their cost and accuracy are critically dependent on the choice of mesh points. To keep computational costs low, mesh relocation (r-adaptivity) seeks to optimise the position of a fixed number of mesh points to obtain the best FE solution accuracy. Classical approaches to this problem require the solution of a separate nonlinear "meshing" PDE to find the mesh point locations. This incurs significant cost at remeshing and relies on certain a-priori assumptions and guiding heuristics for optimal mesh point location. Recent machine learning approaches to r-adaptivity have mainly focused on the construction of fast surrogates for such classical methods. Our new approach combines a graph neural network (GNN) powered architecture, with training based on direct minimisation of the FE solution error with respect to the mesh point locations. The GNN employs graph neural diffusion (GRAND), closely aligning the mesh solution space to that of classical meshing methodologies, thus replacing heuristics with a learnable strategy, and providing a strong inductive bias. This allows for rapid and robust training and results in an extremely efficient and effective GNN approach to online r-adaptivity. This method outperforms classical and prior ML approaches to r-adaptive meshing on the test problems we consider, in particular achieving lower FE solution error, whilst retaining the significant speed-up over classical methods observed in prior ML work. | [
"['James Rowbottom' 'Georg Maierhofer' 'Teo Deveney' 'Katharina Schratz'\n 'Pietro Liò' 'Carola-Bibiane Schönlieb' 'Chris Budd']"
] |
null | null | 2407.04521 | null | null | http://arxiv.org/pdf/2407.04521v1 | 2024-07-05T14:06:59Z | 2024-07-05T14:06:59Z | Unified continuous-time q-learning for mean-field game and mean-field
control problems | This paper studies the continuous-time q-learning in the mean-field jump-diffusion models from the representative agent's perspective. To overcome the challenge when the population distribution may not be directly observable, we introduce the integrated q-function in decoupled form (decoupled Iq-function) and establish its martingale characterization together with the value function, which provides a unified policy evaluation rule for both mean-field game (MFG) and mean-field control (MFC) problems. Moreover, depending on the task to solve the MFG or MFC problem, we can employ the decoupled Iq-function by different means to learn the mean-field equilibrium policy or the mean-field optimal policy respectively. As a result, we devise a unified q-learning algorithm for both MFG and MFC problems by utilizing all test policies stemming from the mean-field interactions. For several examples in the jump-diffusion setting, within and beyond the LQ framework, we can obtain the exact parameterization of the decoupled Iq-functions and the value functions, and illustrate our algorithm from the representative agent's perspective with satisfactory performance. | [
"['Xiaoli Wei' 'Xiang Yu' 'Fengyi Yuan']"
] |
null | null | 2407.04522 | null | null | http://arxiv.org/pdf/2407.04522v1 | 2024-07-05T14:07:15Z | 2024-07-05T14:07:15Z | Graph Reinforcement Learning in Power Grids: A Survey | The challenges posed by renewable energy and distributed electricity generation motivate the development of deep learning approaches to overcome the lack of flexibility of traditional methods in power grids use cases. The application of GNNs is particularly promising due to their ability to learn from graph-structured data present in power grids. Combined with RL, they can serve as control approaches to determine remedial grid actions. This review analyses the ability of GRL to capture the inherent graph structure of power grids to improve representation learning and decision making in different power grid use cases. It distinguishes between common problems in transmission and distribution grids and explores the synergy between RL and GNNs. In transmission grids, GRL typically addresses automated grid management and topology control, whereas on the distribution side, GRL concentrates more on voltage regulation. We analyzed the selected papers based on their graph structure and GNN model, the applied RL algorithm, and their overall contributions. Although GRL demonstrate adaptability in the face of unpredictable events and noisy or incomplete data, it primarily serves as a proof of concept at this stage. There are multiple open challenges and limitations that need to be addressed when considering the application of RL to real power grid operation. | [
"['Mohamed Hassouna' 'Clara Holzhüter' 'Pawel Lytaev' 'Josephine Thomas'\n 'Bernhard Sick' 'Christoph Scholz']"
] |
null | null | 2407.04525 | null | null | http://arxiv.org/pdf/2407.04525v1 | 2024-07-05T14:11:28Z | 2024-07-05T14:11:28Z | Enhancing learning in artificial neural networks through cellular
heterogeneity and neuromodulatory signaling | Recent progress in artificial intelligence (AI) has been driven by insights from neuroscience, particularly with the development of artificial neural networks (ANNs). This has significantly enhanced the replication of complex cognitive tasks such as vision and natural language processing. Despite these advances, ANNs struggle with continual learning, adaptable knowledge transfer, robustness, and resource efficiency - capabilities that biological systems handle seamlessly. Specifically, ANNs often overlook the functional and morphological diversity of the brain, hindering their computational capabilities. Furthermore, incorporating cell-type specific neuromodulatory effects into ANNs with neuronal heterogeneity could enable learning at two spatial scales: spiking behavior at the neuronal level, and synaptic plasticity at the circuit level, thereby potentially enhancing their learning abilities. In this article, we summarize recent bio-inspired models, learning rules and architectures and propose a biologically-informed framework for enhancing ANNs. Our proposed dual-framework approach highlights the potential of spiking neural networks (SNNs) for emulating diverse spiking behaviors and dendritic compartments to simulate morphological and functional diversity of neuronal computations. Finally, we outline how the proposed approach integrates brain-inspired compartmental models and task-driven SNNs, balances bioinspiration and complexity, and provides scalable solutions for pressing AI challenges, such as continual learning, adaptability, robustness, and resource-efficiency. | [
"['Alejandro Rodriguez-Garcia' 'Jie Mei' 'Srikanth Ramaswamy']"
] |
null | null | 2407.04528 | null | null | http://arxiv.org/pdf/2407.04528v1 | 2024-07-05T14:16:47Z | 2024-07-05T14:16:47Z | GPT vs RETRO: Exploring the Intersection of Retrieval and
Parameter-Efficient Fine-Tuning | Parameter-Efficient Fine-Tuning (PEFT) and Retrieval-Augmented Generation (RAG) have become popular methods for adapting large language models while minimizing compute requirements. In this paper, we apply PEFT methods (P-tuning, Adapters, and LoRA) to a modified Retrieval-Enhanced Transformer (RETRO) and a baseline GPT model across several sizes, ranging from 823 million to 48 billion parameters. We show that RETRO models outperform GPT models in zero-shot settings due to their unique pre-training process but GPT models have higher performance potential with PEFT. Additionally, our study indicates that 8B parameter models strike an optimal balance between cost and performance and P-tuning lags behind other PEFT techniques. We further provide a comparative analysis of between applying PEFT to an Instruction-tuned RETRO model and base RETRO model. This work presents the first comprehensive comparison of various PEFT methods integrated with RAG, applied to both GPT and RETRO models, highlighting their relative performance. | [
"['Aleksander Ficek' 'Jiaqi Zeng' 'Oleksii Kuchaiev']"
] |
null | null | 2407.04534 | null | null | http://arxiv.org/pdf/2407.04534v1 | 2024-07-05T14:22:13Z | 2024-07-05T14:22:13Z | Introducing 'Inside' Out of Distribution | Detecting and understanding out-of-distribution (OOD) samples is crucial in machine learning (ML) to ensure reliable model performance. Current OOD studies, in general, and in the context of ML, in particular, primarily focus on extrapolatory OOD (outside), neglecting potential cases of interpolatory OOD (inside). This study introduces a novel perspective on OOD by suggesting OOD can be divided into inside and outside cases. In addition, following this framework, we examine the inside-outside OOD profiles of datasets and their impact on ML model performance. Our analysis shows that different inside-outside OOD profiles lead to nuanced declines in ML model performance, highlighting the importance of distinguishing between these two cases for developing effective counter-OOD methods. | [
"['Teddy Lazebnik']"
] |
null | null | 2407.04538 | null | null | http://arxiv.org/pdf/2407.04538v2 | 2024-07-08T14:44:06Z | 2024-07-05T14:24:37Z | PDiscoFormer: Relaxing Part Discovery Constraints with Vision
Transformers | Computer vision methods that explicitly detect object parts and reason on them are a step towards inherently interpretable models. Existing approaches that perform part discovery driven by a fine-grained classification task make very restrictive assumptions on the geometric properties of the discovered parts; they should be small and compact. Although this prior is useful in some cases, in this paper we show that pre-trained transformer-based vision models, such as self-supervised DINOv2 ViT, enable the relaxation of these constraints. In particular, we find that a total variation (TV) prior, which allows for multiple connected components of any size, substantially outperforms previous work. We test our approach on three fine-grained classification benchmarks: CUB, PartImageNet and Oxford Flowers, and compare our results to previously published methods as well as a re-implementation of the state-of-the-art method PDiscoNet with a transformer-based backbone. We consistently obtain substantial improvements across the board, both on part discovery metrics and the downstream classification task, showing that the strong inductive biases in self-supervised ViT models require to rethink the geometric priors that can be used for unsupervised part discovery. | [
"['Ananthu Aniraj' 'Cassio F. Dantas' 'Dino Ienco' 'Diego Marcos']"
] |
null | null | 2407.04540 | null | null | http://arxiv.org/pdf/2407.04540v1 | 2024-07-05T14:25:22Z | 2024-07-05T14:25:22Z | Improved algorithms for learning quantum Hamiltonians, via flat
polynomials | We give an improved algorithm for learning a quantum Hamiltonian given copies of its Gibbs state, that can succeed at any temperature. Specifically, we improve over the work of Bakshi, Liu, Moitra, and Tang [BLMT24], by reducing the sample complexity and runtime dependence to singly exponential in the inverse-temperature parameter, as opposed to doubly exponential. Our main technical contribution is a new flat polynomial approximation to the exponential function, with significantly lower degree than the flat polynomial approximation used in [BLMT24]. | [
"['Shyam Narayanan']"
] |
null | null | 2407.04541 | null | null | http://arxiv.org/pdf/2407.04541v1 | 2024-07-05T14:28:12Z | 2024-07-05T14:28:12Z | PoPreRo: A New Dataset for Popularity Prediction of Romanian Reddit
Posts | We introduce PoPreRo, the first dataset for Popularity Prediction of Romanian posts collected from Reddit. The PoPreRo dataset includes a varied compilation of post samples from five distinct subreddits of Romania, totaling 28,107 data samples. Along with our novel dataset, we introduce a set of competitive models to be used as baselines for future research. Interestingly, the top-scoring model achieves an accuracy of 61.35% and a macro F1 score of 60.60% on the test set, indicating that the popularity prediction task on PoPreRo is very challenging. Further investigations based on few-shot prompting the Falcon-7B Large Language Model also point in the same direction. We thus believe that PoPreRo is a valuable resource that can be used to evaluate models on predicting the popularity of social media posts in Romanian. We release our dataset at https://github.com/ana-rogoz/PoPreRo. | [
"['Ana-Cristina Rogoz' 'Maria Ilinca Nechita' 'Radu Tudor Ionescu']"
] |
null | null | 2407.04542 | null | null | http://arxiv.org/pdf/2407.04542v1 | 2024-07-05T14:29:12Z | 2024-07-05T14:29:12Z | Rethinking Image Compression on the Web with Generative AI | The rapid growth of the Internet, driven by social media, web browsing, and video streaming, has made images central to the Web experience, resulting in significant data transfer and increased webpage sizes. Traditional image compression methods, while reducing bandwidth, often degrade image quality. This paper explores a novel approach using generative AI to reconstruct images at the edge or client-side. We develop a framework that leverages text prompts and provides additional conditioning inputs like Canny edges and color palettes to a text-to-image model, achieving up to 99.8% bandwidth savings in the best cases and 92.6% on average, while maintaining high perceptual similarity. Empirical analysis and a user study show that our method preserves image meaning and structure more effectively than traditional compression methods, offering a promising solution for reducing bandwidth usage and improving Internet affordability with minimal degradation in image quality. | [
"['Shayan Ali Hassan' 'Danish Humair' 'Ihsan Ayyub Qazi' 'Zafar Ayyub Qazi']"
] |
null | null | 2407.04547 | null | null | http://arxiv.org/pdf/2407.04547v1 | 2024-07-05T14:32:52Z | 2024-07-05T14:32:52Z | Real-time Timbre Remapping with Differentiable DSP | Timbre is a primary mode of expression in diverse musical contexts. However, prevalent audio-driven synthesis methods predominantly rely on pitch and loudness envelopes, effectively flattening timbral expression from the input. Our approach draws on the concept of timbre analogies and investigates how timbral expression from an input signal can be mapped onto controls for a synthesizer. Leveraging differentiable digital signal processing, our method facilitates direct optimization of synthesizer parameters through a novel feature difference loss. This loss function, designed to learn relative timbral differences between musical events, prioritizes the subtleties of graded timbre modulations within phrases, allowing for meaningful translations in a timbre space. Using snare drum performances as a case study, where timbral expression is central, we demonstrate real-time timbre remapping from acoustic snare drums to a differentiable synthesizer modeled after the Roland TR-808. | [
"['Jordie Shier' 'Charalampos Saitis' 'Andrew Robertson' 'Andrew McPherson']"
] |
null | null | 2407.04551 | null | null | http://arxiv.org/pdf/2407.04551v1 | 2024-07-05T14:36:19Z | 2024-07-05T14:36:19Z | An AI Architecture with the Capability to Classify and Explain Hardware
Trojans | Hardware trojan detection methods, based on machine learning (ML) techniques, mainly identify suspected circuits but lack the ability to explain how the decision was arrived at. An explainable methodology and architecture is introduced based on the existing hardware trojan detection features. Results are provided for explaining digital hardware trojans within a netlist using trust-hub trojan benchmarks. | [
"['Paul Whitten' 'Francis Wolff' 'Chris Papachristou']"
] |
null | null | 2407.04557 | null | null | http://arxiv.org/pdf/2407.04557v1 | 2024-07-05T14:42:54Z | 2024-07-05T14:42:54Z | Structural Constraint Integration in Generative Model for Discovery of
Quantum Material Candidates | Billions of organic molecules are known, but only a tiny fraction of the functional inorganic materials have been discovered, a particularly relevant problem to the community searching for new quantum materials. Recent advancements in machine-learning-based generative models, particularly diffusion models, show great promise for generating new, stable materials. However, integrating geometric patterns into materials generation remains a challenge. Here, we introduce Structural Constraint Integration in the GENerative model (SCIGEN). Our approach can modify any trained generative diffusion model by strategic masking of the denoised structure with a diffused constrained structure prior to each diffusion step to steer the generation toward constrained outputs. Furthermore, we mathematically prove that SCIGEN effectively performs conditional sampling from the original distribution, which is crucial for generating stable constrained materials. We generate eight million compounds using Archimedean lattices as prototype constraints, with over 10% surviving a multi-staged stability pre-screening. High-throughput density functional theory (DFT) on 26,000 survived compounds shows that over 50% passed structural optimization at the DFT level. Since the properties of quantum materials are closely related to geometric patterns, our results indicate that SCIGEN provides a general framework for generating quantum materials candidates. | [
"['Ryotaro Okabe' 'Mouyang Cheng' 'Abhijatmedhi Chotrattanapituk'\n 'Nguyen Tuan Hung' 'Xiang Fu' 'Bowen Han' 'Yao Wang' 'Weiwei Xie'\n 'Robert J. Cava' 'Tommi S. Jaakkola' 'Yongqiang Cheng' 'Mingda Li']"
] |
null | null | 2407.04559 | null | null | http://arxiv.org/pdf/2407.04559v1 | 2024-07-05T14:48:15Z | 2024-07-05T14:48:15Z | Not (yet) the whole story: Evaluating Visual Storytelling Requires More
than Measuring Coherence, Grounding, and Repetition | Visual storytelling consists in generating a natural language story given a temporally ordered sequence of images. This task is not only challenging for models, but also very difficult to evaluate with automatic metrics since there is no consensus about what makes a story 'good'. In this paper, we introduce a novel method that measures story quality in terms of human likeness regarding three key aspects highlighted in previous work: visual grounding, coherence, and repetitiveness. We then use this method to evaluate the stories generated by several models, showing that the foundation model LLaVA obtains the best result, but only slightly so compared to TAPM, a 50-times smaller visual storytelling model. Upgrading the visual and language components of TAPM results in a model that yields competitive performance with a relatively low number of parameters. Finally, we carry out a human evaluation study, whose results suggest that a 'good' story may require more than a human-like level of visual grounding, coherence, and repetition. | [
"['Aditya K Surikuchi' 'Raquel Fernández' 'Sandro Pezzelle']"
] |
null | null | 2407.04579 | null | null | http://arxiv.org/pdf/2407.04579v1 | 2024-07-05T15:16:25Z | 2024-07-05T15:16:25Z | GOALPlace: Begin with the End in Mind | Co-optimizing placement with congestion is integral to achieving high-quality designs. This paper presents GOALPlace, a new learning-based general approach to improving placement congestion by controlling cell density. Our method efficiently learns from an EDA tool's post-route optimized results and uses an empirical Bayes technique to adapt this goal/target to a specific placer's solutions, effectively beginning with the end in mind. It enhances correlation with the long-running heuristics of the tool's router and timing-opt engine -- while solving placement globally without expensive incremental congestion estimation and mitigation methods. A statistical analysis with a new hierarchical netlist clustering establishes the importance of density and the potential for an adequate cell density target across placements. Our experiments show that our method, integrated as a demonstration inside an academic GPU-accelerated global placer, consistently produces macro and standard cell placements of superior or comparable quality to commercial tools. Our empirical Bayes methodology also allows a substantial quality improvement over state-of-the-art academic mixed-size placers, achieving up to 10x fewer design rule check (DRC) violations, a 5% decrease in wirelength, and a 30% and 60% reduction in worst and total negative slack (WNS/TNS). | [
"['Anthony Agnesina' 'Rongjian Liang' 'Geraldo Pradipta' 'Anand Rajaram'\n 'Haoxing Ren']"
] |
null | null | 2407.04581 | null | null | http://arxiv.org/pdf/2407.04581v1 | 2024-07-05T15:23:43Z | 2024-07-05T15:23:43Z | Leveraging Large Language Models for Integrated
Satellite-Aerial-Terrestrial Networks: Recent Advances and Future Directions | Integrated satellite, aerial, and terrestrial networks (ISATNs) represent a sophisticated convergence of diverse communication technologies to ensure seamless connectivity across different altitudes and platforms. This paper explores the transformative potential of integrating Large Language Models (LLMs) into ISATNs, leveraging advanced Artificial Intelligence (AI) and Machine Learning (ML) capabilities to enhance these networks. We outline the current architecture of ISATNs and highlight the significant role LLMs can play in optimizing data flow, signal processing, and network management to advance 5G/6G communication technologies through advanced predictive algorithms and real-time decision-making. A comprehensive analysis of ISATN components is conducted, assessing how LLMs can effectively address traditional data transmission and processing bottlenecks. The paper delves into the network management challenges within ISATNs, emphasizing the necessity for sophisticated resource allocation strategies, traffic routing, and security management to ensure seamless connectivity and optimal performance under varying conditions. Furthermore, we examine the technical challenges and limitations associated with integrating LLMs into ISATNs, such as data integration for LLM processing, scalability issues, latency in decision-making processes, and the design of robust, fault-tolerant systems. The study also identifies key future research directions for fully harnessing LLM capabilities in ISATNs, which is crucial for enhancing network reliability, optimizing performance, and achieving a truly interconnected and intelligent global network system. | [
"['Shumaila Javaid' 'Ruhul Amin Khalil' 'Nasir Saeed' 'Bin He'\n 'Mohamed-Slim Alouini']"
] |
null | null | 2407.04587 | null | null | http://arxiv.org/pdf/2407.04587v1 | 2024-07-05T15:32:07Z | 2024-07-05T15:32:07Z | Multimodal Classification via Modal-Aware Interactive Enhancement | Due to the notorious modality imbalance problem, multimodal learning (MML) leads to the phenomenon of optimization imbalance, thus struggling to achieve satisfactory performance. Recently, some representative methods have been proposed to boost the performance, mainly focusing on adaptive adjusting the optimization of each modality to rebalance the learning speed of dominant and non-dominant modalities. To better facilitate the interaction of model information in multimodal learning, in this paper, we propose a novel multimodal learning method, called modal-aware interactive enhancement (MIE). Specifically, we first utilize an optimization strategy based on sharpness aware minimization (SAM) to smooth the learning objective during the forward phase. Then, with the help of the geometry property of SAM, we propose a gradient modification strategy to impose the influence between different modalities during the backward phase. Therefore, we can improve the generalization ability and alleviate the modality forgetting phenomenon simultaneously for multimodal learning. Extensive experiments on widely used datasets demonstrate that our proposed method can outperform various state-of-the-art baselines to achieve the best performance. | [
"['Qing-Yuan Jiang' 'Zhouyang Chi' 'Yang Yang']"
] |
null | null | 2407.04589 | null | null | http://arxiv.org/pdf/2407.04589v1 | 2024-07-05T15:38:36Z | 2024-07-05T15:38:36Z | Remembering Everything Makes You Vulnerable: A Limelight on Machine
Unlearning for Personalized Healthcare Sector | As the prevalence of data-driven technologies in healthcare continues to rise, concerns regarding data privacy and security become increasingly paramount. This thesis aims to address the vulnerability of personalized healthcare models, particularly in the context of ECG monitoring, to adversarial attacks that compromise patient privacy. We propose an approach termed "Machine Unlearning" to mitigate the impact of exposed data points on machine learning models, thereby enhancing model robustness against adversarial attacks while preserving individual privacy. Specifically, we investigate the efficacy of Machine Unlearning in the context of personalized ECG monitoring, utilizing a dataset of clinical ECG recordings. Our methodology involves training a deep neural classifier on ECG data and fine-tuning the model for individual patients. We demonstrate the susceptibility of fine-tuned models to adversarial attacks, such as the Fast Gradient Sign Method (FGSM), which can exploit additional data points in personalized models. To address this vulnerability, we propose a Machine Unlearning algorithm that selectively removes sensitive data points from fine-tuned models, effectively enhancing model resilience against adversarial manipulation. Experimental results demonstrate the effectiveness of our approach in mitigating the impact of adversarial attacks while maintaining the pre-trained model accuracy. | [
"['Ahan Chatterjee' 'Sai Anirudh Aryasomayajula' 'Rajat Chaudhari'\n 'Subhajit Paul' 'Vishwa Mohan Singh']"
] |
null | null | 2407.04591 | null | null | http://arxiv.org/pdf/2407.04591v1 | 2024-07-05T15:40:15Z | 2024-07-05T15:40:15Z | Proximal Point Method for Online Saddle Point Problem | This paper focuses on the online saddle point problem, which involves a sequence of two-player time-varying convex-concave games. Considering the nonstationarity of the environment, we adopt the duality gap and the dynamic Nash equilibrium regret as performance metrics for algorithm design. We present three variants of the proximal point method: the Online Proximal Point Method~(OPPM), the Optimistic OPPM~(OptOPPM), and the OptOPPM with multiple predictors. Each algorithm guarantees upper bounds for both the duality gap and dynamic Nash equilibrium regret, achieving near-optimality when measured against the duality gap. Specifically, in certain benign environments, such as sequences of stationary payoff functions, these algorithms maintain a nearly constant metric bound. Experimental results further validate the effectiveness of these algorithms. Lastly, this paper discusses potential reliability concerns associated with using dynamic Nash equilibrium regret as a performance metric. | [
"['Qing-xin Meng' 'Jian-wei Liu']"
] |
null | null | 2407.04600 | null | null | http://arxiv.org/pdf/2407.04600v1 | 2024-07-05T15:48:34Z | 2024-07-05T15:48:34Z | Understanding the Gains from Repeated Self-Distillation | Self-Distillation is a special type of knowledge distillation where the student model has the same architecture as the teacher model. Despite using the same architecture and the same training data, self-distillation has been empirically observed to improve performance, especially when applied repeatedly. For such a process, there is a fundamental question of interest: How much gain is possible by applying multiple steps of self-distillation? To investigate this relative gain, we propose studying the simple but canonical task of linear regression. Our analysis shows that the excess risk achieved by multi-step self-distillation can significantly improve upon a single step of self-distillation, reducing the excess risk by a factor as large as $d$, where $d$ is the input dimension. Empirical results on regression tasks from the UCI repository show a reduction in the learnt model's risk (MSE) by up to 47%. | [
"['Divyansh Pareek' 'Simon S. Du' 'Sewoong Oh']"
] |
null | null | 2407.04605 | null | null | http://arxiv.org/pdf/2407.04605v1 | 2024-07-05T15:53:16Z | 2024-07-05T15:53:16Z | Linear causal disentanglement via higher-order cumulants | Linear causal disentanglement is a recent method in causal representation learning to describe a collection of observed variables via latent variables with causal dependencies between them. It can be viewed as a generalization of both independent component analysis and linear structural equation models. We study the identifiability of linear causal disentanglement, assuming access to data under multiple contexts, each given by an intervention on a latent variable. We show that one perfect intervention on each latent variable is sufficient and in the worst case necessary to recover parameters under perfect interventions, generalizing previous work to allow more latent than observed variables. We give a constructive proof that computes parameters via a coupled tensor decomposition. For soft interventions, we find the equivalence class of latent graphs and parameters that are consistent with observed data, via the study of a system of polynomial equations. Our results hold assuming the existence of non-zero higher-order cumulants, which implies non-Gaussianity of variables. | [
"['Paula Leyes Carreno' 'Chiara Meroni' 'Anna Seigal']"
] |
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