bluuebunny/crossref_metadata_2025 huggingface dataset:
RTSR: A Real-Time Table Structure Recognition Approach
Authors: Nguyen Nam Quan , Pham Xuan Phong , Tran Tuan-Anh
Published: October 2024

Table Structure Recognition (TSR) aims to reconstruct the logical structure of a table to understand semantic information ordered in the table. Many approaches to modeling the TSR problem have been proposed and have achieved promising results. However, most heavy models or complex post-processing approaches require much time and data consumption for inference and training progress. This paper proposes a new TSR approach called RTSR, a robust simplifying modeling. RTSR includes a lightweight backbone and a module to enhance contextual information between rows/columns. We combine two stages in a split-and-merge manner into only one step by reconstructing a table with horizontal and vertical separators. Specifically, we redesign the split stage to identify grid and spanning cells. Our RTSR can run on average at 38.1 FPS while achieving comparable performance with state-of-the-art methods on several benchmark datasets, including SciTSR, PubTabNet, FinTabNet, and WTW.
Progressive Artistic Aesthetic Enhancement For Chinese Ink Painting Style Transfer
Authors: Huang Chihan
Published: October 2024

The translation of artistic style is a challenging yet crucial task for both computer vision and the arts, and the unique attributes of Chinese ink painting—such as its use of negative space, brushwork, ink diffusion, and more—present significant challenges to the application of existing style transfer algorithms. In response to these distinctive characteristics, we propose a progressive artistic aethetic ink painting style transfer method. The progressive multi-scale aesthetic style attention module in the network leverages the complementary benefits of shallow and deep style information to progressively fuse style features across multiple scales. The covariance transform fusion module addresses issues of stylistic disharmony and enhances the aesthetic quality of the style transfer while preserving the content structure effectively. Additionally, we have developed adaptive spatial interpolation module for detailed information finetuning. Finally, we conducted comparative experiments with previous studies as well as ablation studies, and invited 30 experts in art and design to perform manual evaluations. The results demonstrate that our method can achieve more aesthetically pleasing Chinese ink painting style transfers, confirming its effectiveness and artistic integrity.
Backward Explanations via Redefinition of Predicates
Authors: Saulières Léo , Cooper Martin C. , Dupin de Saint-Cyr Florence
Published: October 2024

History eXplanation based on Predicates (HXP), studies the behavior of a Reinforcement Learning (RL) agent in a sequence of agent’s interactions with the environment (a history), through the prism of an arbitrary predicate [21]. To this end, an action importance score is computed for each action in the history. The explanation consists in displaying the most important actions to the user. As the calculation of an action’s importance is #W[1]-hard, it is necessary for long histories to approximate the scores, at the expense of their quality. We therefore propose a new HXP method, called Backward-HXP, to provide explanations for these histories without having to approximate scores. Experiments show the ability of B-HXP to summarise long histories.
Hard to Explain: On the Computational Hardness of In-Distribution Model Interpretation
Authors: Amir Guy , Bassan Shahaf , Katz Guy
Published: October 2024

The ability to interpret Machine Learning (ML) models is becoming increasingly essential. However, despite significant progress in the field, there remains a lack of rigorous characterization regarding the innate interpretability of different models. In an attempt to bridge this gap, recent work has demonstrated that it is possible to formally assess interpretability by studying the computational complexity of explaining the decisions of various models. In this setting, if explanations for a particular model can be obtained efficiently, the model is considered interpretable (since it can be explained “easily”). However, if generating explanations over an ML model is computationally intractable, it is considered uninterpretable. Prior research identified two key factors that influence the complexity of interpreting an ML model: (i) the type of the model (e.g., neural networks, decision trees, etc.); and (ii) the form of explanation (e.g., contrastive explanations, Shapley values, etc.). In this work, we claim that a third, important factor must also be considered for this analysis — the underlying distribution over which the explanation is obtained. Considering the underlying distribution is key in avoiding explanations that are socially misaligned, i.e., convey information that is biased and unhelpful to users. We demonstrate the significant influence of the underlying distribution on the resulting overall interpretation complexity, in two settings: (i) prediction models paired with an external out-of-distribution (OOD) detector; and (ii) prediction models designed to inherently generate socially aligned explanations. Our findings prove that the expressiveness of the distribution can significantly influence the overall complexity of interpretation, and identify essential prerequisites that a model must possess to generate socially aligned explanations. We regard this work as a step towards a rigorous characterization of the complexity of generating explanations for ML models, and towards gaining a mathematical understanding of their interpretability.
Transparent Explainable Logic Layers
Authors: Ragno Alessio , Plantevit Marc , Robardet Celine , Capobianco Roberto
Published: October 2024

Explainable AI seeks to unveil the intricacies of black box models through post-hoc strategies or self-interpretable models. In this paper, we tackle the problem of building layers that are intrinsically explainable through logic rules. In particular, we address current state-of-the-art methods’ lack of fidelity and expressivity by introducing a transparent explainable logic layer (TELL). We propose to constrain a feed-forward layer with positive weights, which, combined with particular activation functions, offer the possibility of a direct translation into logic rules. Additionally, this approach overcomes the limitations of previous models, linked to their applicability to binary data only, by proposing a new way to automatically threshold real values and incorporate the obtained predicates into logic rules. We show that, compared to state-of-the-art, TELL achieves similar classification performances and, at the same time, provides higher explanatory power, measured by the agreement between models’ outputs and the activation of the logic explanations. In addition, TELL offers a broader spectrum of applications thanks to the possibility of its use on real data.
On the Computation of Contrastive Explanations for Boosted Regression Trees
Authors: Audemard Gilles , Lagniez Jean-Marie , Marquis Pierre
Published: October 2024

A contrastive explanation is a local explanation that is looked for when the prediction achieved by an ML model on an input instance x differs from what was foreseen. A contrastive explanation indicates how to change x to another instance xc from which a prediction that complies with the user’s expectations can be obtained. In this paper, we present a constraint-based approach to the generation of contrastive explanations that are suited to regression functions represented by boosted trees. We show how to compute the smallest interval containing all the regression values that are attainable given a set of characteristics of x that are protected (i.e., not amenable to change). We also show how to generate minimal contrastive explanations for x given a target interval, i.e., instances with regression values within the specified interval and that are as close as possible to x. Closeness is captured using user-dependent mappings reflecting preferences about value change for the attributes (or combinations of attributes) considered in the representation of x.
Safety Verification of Tree-Ensemble Policies via Predicate Abstraction
Authors: Jain Chaahat , Cascioli Lorenzo , Devos Laurens , Vinzent Marcel , Steinmetz Marcel , Davis Jesse , Hoffmann Jörg
Published: October 2024

Learned action policies are gaining traction in AI, but come without safety guarantees. Recent work devised a method for safety verification of neural policies via predicate abstraction. Here we extend this approach to policies represented by tree ensembles, through replacing the underlying SMT queries with queries that can be dispatched by Veritas, a reasoning tool dedicated to tree ensembles. The query language supported by Veritas is limited, and we show how to encode richer constraints we need into additional trees and decision variables. We run experiments on benchmarks previously used to evaluate neural policy verification, and we design new benchmarks based on a logistics application at Airbus as well as on a real-world robotics domain. We find that (1) verification with Veritas vastly outperforms verification with Z3 and Gurobi; (2) tree-ensemble policies are much faster to verify than neural policies, while being competitive in policy quality; (3) our techniques are highly complementary to, and often outperform, an encoding of tree-ensemble policy verification into NUXMV.
Structure and Reduction of MCTS for Explainable-AI
Authors: Bustin Ronit , Goldman Claudia V.
Published: October 2024

Complex sequential decision-making planning problems, covering infinite states’ space have been shown to be solvable by AlphaZero type of algorithms. Such an approach that trains a neural model while simulating projection of futures with a Monte Carlo Tree Search algorithm were shown to be applicable to real life planning problems. As such, engineers and users interacting with the resulting policy of behavior might benefit from obtaining automated explanations about these planners’ decisions offline or online. This paper focuses on the information within the Monte Carlo Tree Search data structure. Given its construction, this information contains much of the reasoning of the sequential decision-making algorithm and is essential for its explainability. We show novel methods using information theoretic tools for the simplification and reduction of the Monte Carlo Tree Search and the extraction of information. Such information can be directly used for the construction of human understandable explanations. We show that basic explainability quantities can be calculated with limited additional computational cost, as an integrated part of the Monte Carlo Tree Search construction process. We focus on the theoretical and algorithmic aspects and provide examples of how the methods presented here can be used in the construction of human understandable explanations.
Shielded FOND: Planning with Safety Constraints in Pure-Past Linear Temporal Logic
Authors: Bonassi Luigi , De Giacomo Giuseppe , Gerevini Alfonso Emilio , Scala Enrico
Published: October 2024

In this paper, we introduce Shielded FOND planning (S-FOND), which is the problem of computing a strategy to reach a final-state goal while preserving a safety specification called shield. In particular, we characterize shields as Pure-Past Linear Temporal Logic formulas that must hold in every prefix of a state trace induced by a solution strategy, thus capturing the whole safety fragment of Linear Temporal Logic formulas over finite traces. We propose three solution encodings for handling S-FOND problems: the first, which is our baseline, simply views a shield as a temporally extended goal; the second, instead, blocks the execution of further actions when the shield gets violated, and the third prevents the execution of actions that could violate the shield by using the notion of regression. We formally prove the correctness of each encoding and experimentally prove their effectiveness over a set of benchmark shields.
Monte Carlo Tree Search with State Merging for Reinforcement Learning in Regular Decision Processes
Authors: Paludo Licks Gabriel , Patrizi Fabio , De Giacomo Giuseppe
Published: October 2024

This paper introduces a novel algorithm for Reinforcement Learning (RL) in Regular Decision Processes (RDPs), a model of non-Markovian decision processes where dynamics and rewards depend on regular properties of the history. Our algorithm is inspired by Monte Carlo tree search (MCTS), yet it is improved with state merging capabilities. Performing merges allows us to evolve the tree model into a graph over time, as we periodically perform similarity tests borrowed from automata learning theory to learn states that are equivalent to one another. This results in improved efficiency and scalability over standard MCTS. We present empirical results that demonstrate orders of magnitude performance improvement over the state-of-the-art RL algorithms for RDPs.
Artwork Protection Against Neural Style Transfer Using Locally Adaptive Adversarial Color Attack
Authors: Guo Zhongliang , Dong Junhao , Qian Yifei , Wang Kaixuan , Li Weiye , Guo Ziheng , Wang Yuheng , Li Yanli , Arandjelović Ognjen , Fang Lei
Published: October 2024

Neural style transfer (NST) generates new images by combining the style of one image with the content of another. However, unauthorized NST can exploit artwork, raising concerns about artists’ rights and motivating the development of proactive protection methods. We propose Locally Adaptive Adversarial Color Attack (LAACA), empowering artists to protect their artwork from unauthorized style transfer by processing before public release. By delving into the intricacies of human visual perception and the role of different frequency components, our method strategically introduces frequency-adaptive perturbations in the image. These perturbations significantly degrade the generation quality of NST while maintaining an acceptable level of visual change in the original image, ensuring that potential infringers are discouraged from using the protected artworks, because of its bad NST generation quality. Additionally, existing metrics often overlook the importance of color fidelity in evaluating color-mattered tasks, such as the quality of NST-generated images, which is crucial in the context of artistic works. To comprehensively assess the color-mattered tasks, we propose the Aesthetic Color Distance Metric (ACDM), designed to quantify the color difference of images pre- and post-manipulations. Experimental results confirm that attacking NST using LAACA results in visually inferior style transfer, and the ACDM can efficiently measure color-mattered tasks. By providing artists with a tool to safeguard their intellectual property, our work relieves the socio-technical challenges posed by the misuse of NST in the art community.
TopFormer: Topology-Aware Authorship Attribution of Deepfake Texts with Diverse Writing Styles
Authors: Uchendu Adaku , Le Thai , Lee Dongwon
Published: October 2024

Recent advances in Large Language Models (LLMs) have enabled the generation of open-ended high-quality texts, that are non-trivial to distinguish from human-written texts. We refer to such LLM-generated texts as deepfake texts. There are currently over 72K text generation models in the huggingface model repo. As such, users with malicious intent can easily use these open-sourced LLMs to generate harmful texts and dis/misinformation at scale. To mitigate this problem, a computational method to determine if a given text is a deepfake text or not is desired–i.e., Turing Test (TT). In particular, in this work, we investigate the more general version of the problem, known as Authorship Attribution (AA), in a multi-class setting–i.e., not only determining if a given text is a deepfake text or not but also being able to pinpoint which LLM is the author. We propose TopFormer to improve existing AA solutions by capturing more linguistic patterns in deepfake texts by including a Topological Data Analysis (TDA) layer in the Transformer-based model. We show the benefits of having a TDA layer when dealing with imbalanced, and multi-style datasets, by extracting TDA features from the reshaped pooled_output of our backbone as input. This Transformer-based model captures contextual representations (i.e., semantic and syntactic linguistic features), while TDA captures the shape and structure of data (i.e., linguistic structures). Finally, TopFormer, outperforms all baselines in all 3 datasets, achieving up to 7% increase in Macro F1 score. Our code and datasets are available at: https://github.com/AdaUchendu/topformer
FlexSSL : A Generic and Efficient Framework for Semi-Supervised Learning
Authors: Qin Huiling , Zhan Xianyuan , Li Yuanxun , Zheng Yu
Published: October 2024

Semi-supervised learning holds great promise for many real-world applications, due to its ability to leverage both unlabeled and expensive labeled data. However, most semi-supervised learning algorithms still heavily rely on the limited labeled data to infer and utilize the hidden information from unlabeled data. We note that any semi-supervised learning task under the self-training paradigm also hides an auxiliary task of discriminating label observability. Jointly solving these two tasks allows full utilization of information from both labeled and unlabeled data, thus alleviating the problem of over-reliance on labeled data. This naturally leads to a new generic and efficient learning framework without the reliance on any domain-specific information, which we call FlexSSL. The key idea of FlexSSL is to construct a semi-cooperative “game”, which forges cooperation between a main self-interested semi-supervised learning task and a companion task that infers label observability to facilitate main task training. We show with theoretical derivation of its connection to loss re-weighting on noisy labels. Through evaluations on a diverse range of tasks, we demonstrate that FlexSSL can consistently enhance the performance of semi-supervised learning algorithms.
Revocable Backdoor for Deep Model Trading
Authors: Xu Yiran , Zhong Nan , Qian Zhenxing , Zhang Xinpeng
Published: October 2024

Deep models are being applied in numerous fields and have become a new important digital product. Meanwhile, previous studies have shown that deep models are vulnerable to backdoor attacks, in which compromised models return attacker-desired results when a trigger appears. Backdoor attacks severely break the trustworthiness of deep models. In this paper, we turn this weakness of deep models into a strength, and propose a novel revocable backdoor and deep model trading scenario. Specifically, we aim to compromise deep models without degrading their performance, meanwhile, we can easily detoxify poisoned models without re-training the models. We design specific mask matrices to manage the internal feature maps of the models. These mask matrices can be used to deactivate the backdoors. The revocable backdoor can be adopted in the deep model trading scenario. Sellers train models with revocable backdoors as a trial version. Buyers pay a deposit to sellers and obtain a trial version of the deep model. If buyers are satisfied with the trial version, they pay a final payment to sellers and sellers send mask matrices to buyers to withdraw revocable backdoors. We demonstrate the feasibility and robustness of our revocable backdoor by various datasets and network architectures.
Connecting Permutation Equivariant Neural Networks and Partition Diagrams
Authors: Pearce-Crump Edward
Published: October 2024

Permutation equivariant neural networks are often constructed using tensor powers of Rn as their layer spaces. We show that all of the weight matrices that appear in these neural networks can be obtained from Schur–Weyl duality between the symmetric group and the partition algebra. In particular, we adapt Schur–Weyl duality to derive a simple, diagrammatic method for calculating the weight matrices themselves.
CSAdv: Class-Specific Adversarial Patches for DETR-Style Object Detection
Authors: Xu Yue , Wang Chuanming , Zheng Xiaolong , Huang Yi , Du Peilun , Zhou Zeyuan , Liu Liang , Ma Huadong
Published: October 2024

Remarkable advancements have been made in the field of object detection, and given its widespread application, it is of paramount importance to investigate the robustness of detection models. However, previous methods have primarily focused on models based on Convolutional Neural Networks (CNNs), seriously neglecting the Transformer-based models that develop rapidly but exhibit obvious differences in terms of information processing. Therefore, this paper aims to address this gap by exploring potential attacks arising from the self-attention mechanism inhered in Transformer. Specifically, we propose a novel adversarial attack scenario targeting Transformer-based object detection models, where only objects of specific class fail to be detected, while irrelevant objects remain undisturbed. Therefore, human perception is hard to find errors even with the detector fail. To achieve this goal, we introduce an adversarial patch generation method, termed Class-Specific Adversarial (CSAdv) patches, which simultaneously leverages class probability to attack specific objects and utilizes the output from Transformer decoder structures, Query Output, to protect irrelevant objects. Due to the long-range interactions of Transformer, the adversarial patch does not need to directly cover or closely surround the specific objects. Instead, it achieves remote targeted attacks simply by being placed in the corner of image, which greatly enhances the concealment of patches. Extensive experiments are conducted on various benchmark datasets and Transformer-based baselines, and the experimental results show that CSAdv can effectively mask certain class while keeping other classes as unaffected as far as possible.
Error-Driven Uncertainty Aware Training
Authors: Mendes Pedro , Romano Paolo , Garlan David
Published: October 2024

Neural networks are often overconfident about their predictions, which undermines their reliability and trustworthiness. In this work, we present a novel technique, named Error-Driven Uncertainty Aware Training (EUAT), which aims to enhance the ability of neural classifiers to estimate their uncertainty correctly, namely to be highly uncertain when they output inaccurate predictions and low uncertain when their output is accurate. The EUAT approach operates during the model’s training phase by selectively employing two loss functions depending on whether the training examples are correctly or incorrectly predicted by the model. This allows for pursuing the twofold goal of i) minimizing model uncertainty for correctly predicted inputs and ii) maximizing uncertainty for mispredicted inputs, while preserving the model’s misprediction rate. We evaluate EUAT using diverse neural models and datasets in the image recognition domains considering both non-adversarial and adversarial settings. The results show that EUAT outperforms existing approaches for uncertainty estimation (including other uncertainty-aware training techniques, calibration, ensembles, and DEUP) by providing uncertainty estimates that not only have higher quality when evaluated via statistical metrics (e.g., correlation with residuals) but also when employed to build binary classifiers that decide whether the model’s output can be trusted or not and under distributional data shifts.
Sentinel: An Aggregation Function to Secure Decentralized Federated Learning
Authors: Feng Chao , Huertas Celdrán Alberto , Baltensperger Janosch , Martínez Beltrán Enrique Tomás , Sánchez Sánchez Pedro Miguel , Bovet Gérôme , Stiller Burkhard
Published: October 2024

Decentralized Federated Learning (DFL) emerges as an innovative paradigm to train collaborative models, addressing the single point of failure limitation. However, the security and trustworthiness of FL and DFL are compromised by poisoning attacks, negatively impacting its performance. Existing defense mechanisms have been designed for centralized FL and they do not adequately exploit the particularities of DFL. Thus, this work introduces Sentinel, a defense strategy to counteract poisoning attacks in DFL. Sentinel leverages the accessibility of local data and defines a three-step aggregation protocol consisting of similarity filtering, bootstrap validation, and normalization to safeguard against malicious model updates. Sentinel has been evaluated with diverse datasets and data distributions. Besides, various poisoning attack types and threat levels have been verified. The results improve the state-of-the-art performance against both untargeted and targeted poisoning attacks when data follows an IID (Independent and Identically Distributed) configuration. Besides, under non-IID configuration, it is analyzed how performance degrades both for Sentinel and other state-of-the-art robust aggregation methods.
Learning Confidence Bounds for Classification with Imbalanced Data
Authors: Clifford Matt , Erskine Jonathan , Hepburn Alexander , Santos-Rodríguez Raúl , Garcia-Garcia Dario
Published: October 2024

Class imbalance poses a significant challenge in classification tasks, where traditional approaches often lead to biased models and unreliable predictions. Undersampling and oversampling techniques have been commonly employed to address this issue, yet they suffer from inherent limitations stemming from their simplistic approach such as loss of information and additional biases respectively. In this paper, we propose a novel framework that leverages learning theory and concentration inequalities to overcome the shortcomings of traditional solutions. We focus on understanding the uncertainty in a class-dependent manner, as captured by confidence bounds that we directly embed into the learning process. By incorporating class-dependent estimates, our method can effectively adapt to the varying degrees of imbalance across different classes, resulting in more robust and reliable classification outcomes. We empirically show how our framework provides a promising direction for handling imbalanced data in classification tasks, offering practitioners a valuable tool for building more accurate and trustworthy models.
A Parallel Gumbel-Softmax VAE Framework with Performance-Based Tuning
Authors: Zhou Fangshi , Zhao Tianming , Nguyen Luan Viet , Yao Zhongmei
Published: October 2024

Traditional training algorithms for Gumbel Softmax Variational Autoencoders (GS-VAEs) typically rely on an annealing scheme that gradually reduces the Softmax temperature τ according to a given function. This approach can lead to suboptimal results. To improve the performance, we propose a parallel framework for GS-VAEs, which embraces dual latent layers and multiple sub-models with diverse temperature strategies. Instead of relying on a fixed function for adjusting τ, our training algorithm uses loss difference as performance feedback to dynamically update each sub-model’s temperature τ, which is inspired by the need to balance exploration and exploitation in learning. By combining diversity in temperature strategies with the performance-based tuning method, our design helps prevent sub-models from becoming trapped in local optima and finds the GS-VAE model that best fits the given dataset. In experiments using four classic image datasets, our model significantly surpasses a standard GS-VAE that employs a temperature annealing scheme across multiple tasks, including data reconstruction, generalization capabilities, anomaly detection, and adversarial robustness. Our implementation is publicly available at https://github.com/wxzg7045/Gumbel-Softmax-VAE-2024/tree/main.
CLCE: An Approach to Refining Cross-Entropy and Contrastive Learning for Optimized Learning Fusion
Authors: Long Zijun , Zhuang Lipeng , Killick George , Meng Zaiqiao , Mccreadie Richard , Aragon-Camarasa Gerardo
Published: October 2024

State-of-the-art pre-trained image models predominantly adopt a two-stage approach: initial unsupervised pre-training on large-scale datasets followed by task-specific fine-tuning using Cross-Entropy loss (CE). However, it has been demonstrated that CE can compromise model generalization and stability. While recent works employing contrastive learning address some of these limitations by enhancing the quality of embeddings and producing better decision boundaries, they often overlook the importance of hard negative mining and rely on resource intensive and slow training using large sample batches. To counter these issues, we introduce a novel approach named CLCE, which integrates Label-Aware Contrastive Learning with CE. Our approach not only maintains the strengths of both loss functions but also leverages hard negative mining in a synergistic way to enhance performance. Experimental results demonstrate that CLCE significantly outperforms CE in Top-1 accuracy across twelve benchmarks, achieving gains of up to 3.52% in few-shot learning scenarios and 3.41% in transfer learning settings with the BEiT-3 model. Importantly, our proposed CLCE approach effectively mitigates the dependency of contrastive learning on large batch sizes such as 4096 samples per batch, a limitation that has previously constrained the application of contrastive learning in budget-limited hardware environments.
Large Language Models Understand Layout
Authors: Li Weiming , Duan Manni , An Dong , Shao Yan
Published: October 2024

Large language models (LLMs) demonstrate extraordinary abilities in a wide range of natural language processing (NLP) tasks. In this paper, we show that, beyond text understanding capability, LLMs are capable of processing text layouts that are denoted by spatial markers. They are able to answer questions that require explicit spatial perceiving and reasoning, while a drastic performance drop is observed when the spatial markers from the original data are excluded. We perform a series of experiments with the GPT-3.5/4, Baichuan2, Llama2, and ChatGLM3 models on various types of layout-sensitive datasets for further analysis. The experimental results reveal that the layout understanding ability of LLMs is mainly introduced by the coding data for pre-training, which is further enhanced at the instruction-tuning stage. In addition, layout understanding can be enhanced by integrating low-cost, auto-generated data approached by a novel text game. Finally, we show that layout understanding ability is beneficial for building efficient visual question-answering (VQA) systems.
Optimizing Multi-Robot Task Allocation in Dynamic Environments via Heuristic-Guided Reinforcement Learning
Authors: Pal Aritra , Chauhan Anandsingh , Baranwal Mayank , Ojha Ankush
Published: October 2024

In modern warehousing environments, efficient task allocation among multiple robots is crucial for optimizing productivity and meeting the ever-increasing demands of online order fulfillment. In this paper, we address the challenging problem of real-time multi-robot task allocation (MRTA) in a warehouse setting, where tasks appear dynamically with corresponding start and end locations. The objective is to minimize both the total travel distance of robots and the delay in task execution while considering practical charging/discharging constraints and collision-free navigation. To tackle this combinatorially hard problem, we propose a heuristic guided reinforcement learning (RL) agent, HeuRAL-MATE, which learns to prioritize prompt task execution while optimizing the assignment of tasks to robots. Our proposed approach outperforms standard practices like First-In-First-Out (FIFO), as well as a brute-force optimal approach in terms of efficiency and performance. The results on multiple synthetic datasets exhibit an average cost reduction of approximately 8.58% and 10.74% in total expenses when compared with brute-force optimal approach and FIFO, respectively.
Anti-Matthew FL: Bridging the Performance Gap in Federated Learning to Counteract the Matthew Effect
Authors: Gao Jiashi , Yao Xin , Wei Xuetao
Published: October 2024

Federated learning (FL) stands as a paradigmatic approach that facilitates model training across heterogeneous and diverse datasets originating from various data providers. However, conventional FLs fall short of achieving consistent performance, potentially leading to performance degradation for clients who are disadvantaged in data resources. Influenced by the Matthew effect, deploying a performance-imbalanced global model in applications further impedes the generation of high-quality data from disadvantaged clients, exacerbating the disparities in data resources among clients. In this work, we propose anti-Matthew fairness for the global model at the client level, requiring equal accuracy and equal decision bias across clients. To balance the trade-off between achieving anti-Matthew fairness and performance optimality, we formalize the anti-Matthew effect federated learning (anti-Matthew FL) as a multi-constrained multi-objectives optimization (MCMOO) problem and propose a three-stage multi-gradient descent algorithm to obtain the Pareto optimality. We theoretically analyze the convergence and time complexity of our proposed algorithms. Additionally, through extensive experimentation, we demonstrate that our proposed anti-Matthew FL outperforms other state-of-the-art FL algorithms in achieving a high-performance global model while effectively bridging performance gaps among clients. We hope this work provides valuable insights into the manifestation of the Matthew effect in FL and other decentralized learning scenarios and can contribute to designing fairer learning mechanisms, ultimately fostering societal welfare.
Dirichlet Logistic Gaussian Processes for Evaluation of Black-Box Stochastic Systems under Complex Requirements
Authors: Oura Ryohei , Ito Yuji
Published: October 2024

The requirement-driven performance evaluation of a black-box cyber-physical system (CPS) that utilizes machine learning methods has proven to be an effective way to assess the quality of the CPS. However, the distributional evaluation of the performance has been poorly considered. Although many uncertainty estimation methods have been advocated, they have not successfully estimated highly complex performance distributions under small data. In this paper, we propose a method to distributionally evaluate the performance under complex requirements using small input-trajectory data. To handle the unknown complex probability distributions under small data, we discretize the corresponding performance measure, yielding a discrete random process over an input region. Then, we propose a semiparametric Bayesian model of the discrete process based on a Dirichlet random field whose parameter function is represented by multiple logistic Gaussian processes (LGPs). The Dirichlet posterior parameter function is estimated through the LGP posteriors in a reasonable and conservative fashion. We show that the proposed Bayesian model converges to the true discrete random process as the number of data becomes large enough. We also empirically demonstrate the effectiveness of the proposed method by simulation.
Graph Classification with GNNs: Optimisation, Representation & Inductive Bias
Authors: Kumar P. Krishna , Ramaswamy Harish G.
Published: October 2024

Theoretical studies on the representation power of GNNs have been centered around understanding the equivalence of GNNs, using WL-Tests for detecting graph isomorphism. In this paper, we argue that such equivalence ignores the accompanying optimization issues and does not provide a holistic view of the GNN learning process. We illustrate these gaps between representation and optimization with examples and experiments. We also explore the existence of an implicit inductive bias (e.g. fully connected networks prefer to learn low frequency functions in their input space) in GNNs, in the context of graph classification tasks. We further prove theoretically that the message-passing layers in the graph, have a tendency to search for either discriminative subgraphs, or a collection of discriminative nodes dispersed across the graph, depending on the different global pooling layers used. We empirically verify this bias through experiments over real-world and synthetic datasets. Finally, we show how our work can help in incorporating domain knowledge via attention based architectures, and can evince their capability to discriminate coherent subgraphs.
Harnessing Orthogonality to Train Low-Rank Neural Networks
Authors: Coquelin Daniel , Flügel Katharina , Weiel Marie , Kiefer Nicholas , Debus Charlotte , Streit Achim , Götz Markus
Published: October 2024

This study explores the learning dynamics of neural networks by analyzing the singular value decomposition (SVD) of their weights throughout training. Our investigation reveals that an orthogonal basis within each multidimensional weight’s SVD representation stabilizes during training. Building upon this, we introduce Orthogonality-Informed Adaptive Low-Rank (OIALR) training, a novel training method exploiting the intrinsic orthogonality of neural networks. OIALR seamlessly integrates into existing training workflows with minimal accuracy loss, as demonstrated by benchmarking on various datasets and well-established network architectures. With appropriate hyperparameter tuning, OIALR can surpass conventional training setups, including those of state-of-the-art models.
FedSeProto: Learning Semantic Prototype in Federated Learning
Authors: Lai Yanyi , Fu Lele , Liao Tianchi , Chen Chuan , Zheng Zibin
Published: October 2024

Federated learning enables multiple clients to collaboratively train a global model without revealing their local data. However, conventional federated learning often overlooks the fact that data stored on different clients may originate from diverse domains, and the resulting domain shift problem can significantly impair the performance of the global model. In this paper, we introduce Federated Semantic Prototype Learning (FedSeProto), a semantic prototype-based approach designed to address the domain shift issue in federated learning. The proposed method comprises two components: feature decoupling and feature alignment. Feature decoupling aims to learn semantic prototypes that can represent semantic information associated with specific categories, while feature alignment utilizes these semantic prototypes to facilitate learning of cross-client consistent features. Two key techniques are employed to achieve feature decoupling. On one hand, feature separation is achieved through the minimization of mutual information between semantic and domain features. On the other hand, the knowledge distillation is leveraged to ensure that both semantic and domain features carry the correct information. For feature alignment, intra-class semantic features are used to generate the local prototypes, which are further aggregated to the global prototypes. These global prototypes serve as guides during the local training process. Specifically, the local intra-class semantic features are driven to close to the corresponding global prototypes, thereby encouraging all clients to learn the globally consistent semantic features. Comprehensive experiments conducted on four challenging multi-domain datasets demonstrate the effectiveness of the proposed method compared with existing federated learning algorithms.
Zero-Shot Relabeling of Weak Labels for Fine-Grained Semantic Indexing of Biomedical Literature
Authors: Nentidis Anastasios , Krithara Anastasia , Tsoumakas Grigorios , Paliouras Georgios
Published: October 2024

We address the need for focused retrieval and integration of biomedical literature, enabling improved subject annotation of documents with domain concepts for which no manual labels are available. To do so, we propose a novel zero-shot method, called PN Relabeler, that improves heuristic concept-level annotations by relabeling the documents Predicted as Negative (PN) by the concept occurrence (CO) heuristic. CO has proven to be a good heuristic for fine-grained semantic indexing (FGSI) of biomedical literature. In particular, it is quite precise but still misses some document labels, i.e. it suffers from lower recall. The PN Relabeler method addresses this problem, by introducing a novel approach to combine heuristic annotations for unseen labels with knowledge learned from past labels. To do so, it first tackles the intermediate task of zero-shot relabeling of documents labeled with CO-based FGSI annotations. Then, it builds upon the power of domain-specific deep pretrained language models to improve the recall of the heuristic annotation, i.e. reduce the rate of false negative cases. The results reveal that relabeling with PN Relabeler improves the micro-F1 by more than 6%, compared to CO annotations, and by 3%, compared to state-of-the-art CO-based FGSI methods. This highlights the potential of learning from the errors of a strong heuristic on concepts where manual labels are already available. This knowledge is useful for improving the heuristic FGSI labels on new unseen concepts without manual labels in a zero-shot scenario.
Exploiting Hierarchical Symmetry in Multi-Agent Reinforcement Learning
Authors: Tian Yongkai , Yu Xin , Qi Yirong , Wang Li , Feng Pu , Wu Wenjun , Shi Rongye , Luo Jie
Published: October 2024

Achieving high sample efficiency is a critical research area in reinforcement learning. This becomes extremely difficult in multi-agent reinforcement learning (MARL), as the capacity of the joint state and action space grows exponentially with the number of agents. The reliance of MARL solely on exploration and trial-and-error, without incorporating prior knowledge, exacerbates the issue of low sample efficiency. Currently, introducing symmetry into MARL is an effective approach to address this issue. Yet the concept of hierarchical symmetry, which maintains symmetry across different levels of a multi-agent system (MAS), has not been explored in existing methods. This paper focuses on multi-agent cooperative tasks and proposes a method incorporating hierarchical symmetry, termed the Hierarchical Equivariant Policy Network (HEPN) which is O(n)-equivariant. Specifically, HEPN utilizes clustering to perform hierarchical information extraction in MAS, and employs graph neural networks to model agent interactions. We conducted extensive experiments across various multi-agent tasks. The results indicate that our method achieves faster convergence speeds and higher convergence rewards compared to baseline algorithms. Additionally, we have deployed our algorithm in a physical multi-robot system, confirming its effectiveness in real-world environments. Supplementary materials are available at https://yongkai-tian.github.io/HEPN/.
Bottlenecked Backpropagation to Train Differentially Private Deep Neural Networks
Authors: Ghosh Arghyadeep , Das Mrinal
Published: October 2024

Deep neural networks often tend to memorize data and can risk information leakage if trained on private data. There has been some attempts to train deep neural networks by contaminating the gradients during backpropagation. Over the years, this has become one of the most prominent techniques to make neural networks differentially private. One downside of this method is that contaminated gradients lead to suboptimal solutions during backpropagation. In this paper, we make an attempt to diminish the contamination effect by proposing a bottlenecked backpropagation technique. The proposed bottlenecked backpropagation technique follows Reny differential privacy, a recently developed more optimized version in the realm of differential privacy. On the other hand, the bottlenecked backpropagation considers the direction and neglects the magnitude of the gradient vectors. The idea is built on top of signed stochastic gradient descent, another recent advancement in the optimization methods for deep learning. By prioritizing gradient direction over magnitude, it minimizes noise impact on model convergence. Experimental results on benchmarks including MNIST, FMNIST, and IMDB datasets demonstrate substantial improvements over the state-of-the-art methods, achieving faster convergence and higher model accuracy, striking a promising balance between privacy and performance. Furthermore, we observe the proposed methods to be resilient against membership inference attacks.
Differentiable Neural Network for Assembling Blocks
Authors: Liu Zhiwei , Lu Qiang , Zhao Yibo , Zhao Yanhong , Luo Jake
Published: October 2024

The goal of assembly blocks is to select blocks from pre-trained neural network (NN) models and combine them into a new NN for a different dataset. By reusing the weights of these blocks, training the NN with the new dataset becomes cost-effective. To achieve this goal, we propose an end-to-end differentiable neural network called PA-DNN. PA-DNN consists of two modules: a partition NN module and an assembly NN module. For the new dataset, the partition NN module divides existing pre-trained NN models into blocks. The assembly NN module then selects some of these blocks and combines them into a new NN using a stitching component. To train PA-DNN, we design a score function that evaluates the performance of each new NN generated by PA-DNN. The evaluated value is used to train the partition NN module. Additionally, two loss functions are created to train the assembly NN module and the stitching component in the new NN, respectively. After the training process, PA-DNN infers a new NN, and only the stitching component of the NN is fine-tuned with the new dataset. Experiments show that, compared to manual models, neural architecture search, and the assembly model DeRy, PA-DNN can generate a more accurate and lightweight NN with lower training costs.
Learning Reward Structure with Subtasks in Reinforcement Learning
Authors: Han Shuai , Dastani Mehdi , Wang Shihan
Published: October 2024

Improving sample efficiency of Reinforcement Learning (RL) in sparse-reward environments poses a significant challenge. In scenarios where the reward structure is complex, accurate action evaluation often relies heavily on precise information about past achieved subtasks and their order. Previous approaches have often failed or proved inefficient in constructing and leveraging such intricate reward structures. In this work, we propose an RL algorithm that can automatically structure the reward function for sample efficiency, given a set of labels that signify subtasks. Given such minimal knowledge about the task, we train a high-level policy that selects optimal subtasks in each state together with a low-level policy that efficiently learns to complete each sub-task. We evaluate our algorithm in a variety of sparse-reward environments. The experiment results show that our method significantly outperforms the state-of-art baselines as the difficulty of the task increases.
Verification of Geometric Robustness of Neural Networks via Piecewise Linear Approximation and Lipschitz Optimisation
Authors: Batten Ben , Zheng Yang , De Palma Alessandro , Kouvaros Panagiotis , Lomuscio Alessio
Published: October 2024

We address the problem of verifying neural networks against geometric transformations of the input image, including rotation, scaling, shearing, and translation. The proposed method computes provably sound piecewise linear constraints for the pixel values by using sampling and linear approximations in combination with branch-and-bound Lipschitz optimisation. The method obtains provably tighter over-approximations of the perturbation region than the present state-of-the-art. We report results from experiments on a comprehensive set of verification benchmarks on MNIST and CIFAR10. We show that our proposed implementation resolves up to 32% more verification cases than present approaches.