categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2405.01838 | null | null | http://arxiv.org/pdf/2405.01838v1 | 2024-05-03T04:08:15Z | 2024-05-03T04:08:15Z | A Novel Approach to Guard from Adversarial Attacks using Stable
Diffusion | Recent developments in adversarial machine learning have highlighted the importance of building robust AI systems to protect against increasingly sophisticated attacks. While frameworks like AI Guardian are designed to defend against these threats, they often rely on assumptions that can limit their effectiveness. For example, they may assume attacks only come from one direction or include adversarial images in their training data. Our proposal suggests a different approach to the AI Guardian framework. Instead of including adversarial examples in the training process, we propose training the AI system without them. This aims to create a system that is inherently resilient to a wider range of attacks. Our method focuses on a dynamic defense strategy using stable diffusion that learns continuously and models threats comprehensively. We believe this approach can lead to a more generalized and robust defense against adversarial attacks. In this paper, we outline our proposed approach, including the theoretical basis, experimental design, and expected impact on improving AI security against adversarial threats. | [
"['Trinath Sai Subhash Reddy Pittala' 'Uma Maheswara Rao Meleti'\n 'Geethakrishna Puligundla']"
]
|
null | null | 2405.01843 | null | null | http://arxiv.org/pdf/2405.01843v3 | 2024-06-11T06:32:16Z | 2024-05-03T04:26:03Z | Closing the Gap: Achieving Global Convergence (Last Iterate) of
Actor-Critic under Markovian Sampling with Neural Network Parametrization | The current state-of-the-art theoretical analysis of Actor-Critic (AC) algorithms significantly lags in addressing the practical aspects of AC implementations. This crucial gap needs bridging to bring the analysis in line with practical implementations of AC. To address this, we advocate for considering the MMCLG criteria: textbf{M}ulti-layer neural network parametrization for actor/critic, textbf{M}arkovian sampling, textbf{C}ontinuous state-action spaces, the performance of the textbf{L}ast iterate, and textbf{G}lobal optimality. These aspects are practically significant and have been largely overlooked in existing theoretical analyses of AC algorithms. In this work, we address these gaps by providing the first comprehensive theoretical analysis of AC algorithms that encompasses all five crucial practical aspects (covers MMCLG criteria). We establish global convergence sample complexity bounds of $tilde{mathcal{O}}left({epsilon^{-3}}right)$. We achieve this result through our novel use of the weak gradient domination property of MDP's and our unique analysis of the error in critic estimation. | [
"['Mudit Gaur' 'Amrit Singh Bedi' 'Di Wang' 'Vaneet Aggarwal']"
]
|
null | null | 2405.01848 | null | null | http://arxiv.org/pdf/2405.01848v1 | 2024-05-03T04:43:24Z | 2024-05-03T04:43:24Z | RankSHAP: a Gold Standard Feature Attribution Method for the Ranking
Task | Several works propose various post-hoc, model-agnostic explanations for the task of ranking, i.e. the task of ordering a set of documents, via feature attribution methods. However, these attributions are seen to weakly correlate and sometimes contradict each other. In classification/regression, several works focus on emph{axiomatic characterization} of feature attribution methods, showing that a certain method uniquely satisfies a set of desirable properties. However, no such efforts have been taken in the space of feature attributions for the task of ranking. We take an axiomatic game-theoretic approach, popular in the feature attribution community, to identify candidate attribution methods for ranking tasks. We first define desirable axioms: Rank-Efficiency, Rank-Missingness, Rank-Symmetry and Rank-Monotonicity, all variants of the classical Shapley axioms. Next, we introduce Rank-SHAP, a feature attribution algorithm for the general ranking task, which is an extension to classical Shapley values. We identify a polynomial-time algorithm for computing approximate Rank-SHAP values and evaluate the computational efficiency and accuracy of our algorithm under various scenarios. We also evaluate its alignment with human intuition with a user study. Lastly, we theoretically examine popular rank attribution algorithms, EXS and Rank-LIME, and evaluate their capacity to satisfy the classical Shapley axioms. | [
"['Tanya Chowdhury' 'Yair Zick' 'James Allan']"
]
|
null | null | 2405.01849 | null | null | http://arxiv.org/pdf/2405.01849v1 | 2024-05-03T04:44:51Z | 2024-05-03T04:44:51Z | Stability of Explainable Recommendation | Explainable Recommendation has been gaining attention over the last few years in industry and academia. Explanations provided along with recommendations in a recommender system framework have many uses: particularly reasoning why a suggestion is provided and how well an item aligns with a user's personalized preferences. Hence, explanations can play a huge role in influencing users to purchase products. However, the reliability of the explanations under varying scenarios has not been strictly verified from an empirical perspective. Unreliable explanations can bear strong consequences such as attackers leveraging explanations for manipulating and tempting users to purchase target items that the attackers would want to promote. In this paper, we study the vulnerability of existent feature-oriented explainable recommenders, particularly analyzing their performance under different levels of external noises added into model parameters. We conducted experiments by analyzing three important state-of-the-art (SOTA) explainable recommenders when trained on two widely used e-commerce based recommendation datasets of different scales. We observe that all the explainable models are vulnerable to increased noise levels. Experimental results verify our hypothesis that the ability to explain recommendations does decrease along with increasing noise levels and particularly adversarial noise does contribute to a much stronger decrease. Our study presents an empirical verification on the topic of robust explanations in recommender systems which can be extended to different types of explainable recommenders in RS. | [
"['Sairamvinay Vijayaraghavan' 'Prasant Mohapatra']"
]
|
null | null | 2405.01851 | null | null | http://arxiv.org/pdf/2405.01851v1 | 2024-05-03T04:47:23Z | 2024-05-03T04:47:23Z | Deep Learning Inference on Heterogeneous Mobile Processors: Potentials
and Pitfalls | There is a growing demand to deploy computation-intensive deep learning (DL) models on resource-constrained mobile devices for real-time intelligent applications. Equipped with a variety of processing units such as CPUs, GPUs, and NPUs, the mobile devices hold potential to accelerate DL inference via parallel execution across heterogeneous processors. Various efficient parallel methods have been explored to optimize computation distribution, achieve load balance, and minimize communication cost across processors. Yet their practical effectiveness in the dynamic and diverse real-world mobile environment is less explored. This paper presents a holistic empirical study to assess the capabilities and challenges associated with parallel DL inference on heterogeneous mobile processors. Through carefully designed experiments covering various DL models, mobile software/hardware environments, workload patterns, and resource availability, we identify limitations of existing techniques and highlight opportunities for cross-level optimization. | [
"['Sicong Liu' 'Wentao Zhou' 'Zimu Zhou' 'Bin Guo' 'Minfan Wang'\n 'Cheng Fang' 'Zheng Lin' 'Zhiwen Yu']"
]
|
null | null | 2405.01855 | null | null | http://arxiv.org/pdf/2405.01855v1 | 2024-05-03T05:03:07Z | 2024-05-03T05:03:07Z | Robust Explainable Recommendation | Explainable Recommender Systems is an important field of study which provides reasons behind the suggested recommendations. Explanations with recommender systems are useful for developers while debugging anomalies within the system and for consumers while interpreting the model's effectiveness in capturing their true preferences towards items. However, most of the existing state-of-the-art (SOTA) explainable recommenders could not retain their explanation capability under noisy circumstances and moreover are not generalizable across different datasets. The robustness of the explanations must be ensured so that certain malicious attackers do not manipulate any high-stake decision scenarios to their advantage, which could cause severe consequences affecting large groups of interest. In this work, we present a general framework for feature-aware explainable recommenders that can withstand external attacks and provide robust and generalized explanations. This paper presents a novel framework which could be utilized as an additional defense tool, preserving the global explainability when subject to model-based white box attacks. Our framework is simple to implement and supports different methods regardless of the internal model structure and intrinsic utility within any model. We experimented our framework on two architecturally different feature-based SOTA explainable algorithms by training them on three popular e-commerce datasets of increasing scales. We noticed that both the algorithms displayed an overall improvement in the quality and robustness of the global explainability under normal as well as noisy environments across all the datasets, indicating the flexibility and mutability of our framework. | [
"['Sairamvinay Vijayaraghavan' 'Prasant Mohapatra']"
]
|
null | null | 2405.01859 | null | null | http://arxiv.org/pdf/2405.01859v2 | 2024-05-31T23:28:13Z | 2024-05-03T05:19:45Z | AI-Powered Autonomous Weapons Risk Geopolitical Instability and Threaten
AI Research | The recent embrace of machine learning (ML) in the development of autonomous weapons systems (AWS) creates serious risks to geopolitical stability and the free exchange of ideas in AI research. This topic has received comparatively little attention of late compared to risks stemming from superintelligent artificial general intelligence (AGI), but requires fewer assumptions about the course of technological development and is thus a nearer-future issue. ML is already enabling the substitution of AWS for human soldiers in many battlefield roles, reducing the upfront human cost, and thus political cost, of waging offensive war. In the case of peer adversaries, this increases the likelihood of "low intensity" conflicts which risk escalation to broader warfare. In the case of non-peer adversaries, it reduces the domestic blowback to wars of aggression. This effect can occur regardless of other ethical issues around the use of military AI such as the risk of civilian casualties, and does not require any superhuman AI capabilities. Further, the military value of AWS raises the specter of an AI-powered arms race and the misguided imposition of national security restrictions on AI research. Our goal in this paper is to raise awareness among the public and ML researchers on the near-future risks posed by full or near-full autonomy in military technology, and we provide regulatory suggestions to mitigate these risks. We call upon AI policy experts and the defense AI community in particular to embrace transparency and caution in their development and deployment of AWS to avoid the negative effects on global stability and AI research that we highlight here. | [
"['Riley Simmons-Edler' 'Ryan Badman' 'Shayne Longpre' 'Kanaka Rajan']"
]
|
null | null | 2405.01873 | null | null | http://arxiv.org/pdf/2405.01873v1 | 2024-05-03T06:06:01Z | 2024-05-03T06:06:01Z | Enhancing Bangla Language Next Word Prediction and Sentence Completion
through Extended RNN with Bi-LSTM Model On N-gram Language | Texting stands out as the most prominent form of communication worldwide. Individual spend significant amount of time writing whole texts to send emails or write something on social media, which is time consuming in this modern era. Word prediction and sentence completion will be suitable and appropriate in the Bangla language to make textual information easier and more convenient. This paper expands the scope of Bangla language processing by introducing a Bi-LSTM model that effectively handles Bangla next-word prediction and Bangla sentence generation, demonstrating its versatility and potential impact. We proposed a new Bi-LSTM model to predict a following word and complete a sentence. We constructed a corpus dataset from various news portals, including bdnews24, BBC News Bangla, and Prothom Alo. The proposed approach achieved superior results in word prediction, reaching 99% accuracy for both 4-gram and 5-gram word predictions. Moreover, it demonstrated significant improvement over existing methods, achieving 35%, 75%, and 95% accuracy for uni-gram, bi-gram, and tri-gram word prediction, respectively | [
"['Md Robiul Islam' 'Al Amin' 'Aniqua Nusrat Zereen']"
]
|
null | null | 2405.01881 | null | null | http://arxiv.org/pdf/2405.01881v2 | 2024-05-06T04:06:57Z | 2024-05-03T06:56:47Z | Explainable Risk Classification in Financial Reports | Every publicly traded company in the US is required to file an annual 10-K financial report, which contains a wealth of information about the company. In this paper, we propose an explainable deep-learning model, called FinBERT-XRC, that takes a 10-K report as input, and automatically assesses the post-event return volatility risk of its associated company. In contrast to previous systems, our proposed model simultaneously offers explanations of its classification decision at three different levels: the word, sentence, and corpus levels. By doing so, our model provides a comprehensive interpretation of its prediction to end users. This is particularly important in financial domains, where the transparency and accountability of algorithmic predictions play a vital role in their application to decision-making processes. Aside from its novel interpretability, our model surpasses the state of the art in predictive accuracy in experiments on a large real-world dataset of 10-K reports spanning six years. | [
"['Xue Wen Tan' 'Stanley Kok']"
]
|
null | null | 2405.01883 | null | null | http://arxiv.org/abs/2405.01883v1 | 2024-05-03T07:04:26Z | 2024-05-03T07:04:26Z | DALLMi: Domain Adaption for LLM-based Multi-label Classifier | Large language models (LLMs) increasingly serve as the backbone for classifying text associated with distinct domains and simultaneously several labels (classes). When encountering domain shifts, e.g., classifier of movie reviews from IMDb to Rotten Tomatoes, adapting such an LLM-based multi-label classifier is challenging due to incomplete label sets at the target domain and daunting training overhead. The existing domain adaptation methods address either image multi-label classifiers or text binary classifiers. In this paper, we design DALLMi, Domain Adaptation Large Language Model interpolator, a first-of-its-kind semi-supervised domain adaptation method for text data models based on LLMs, specifically BERT. The core of DALLMi is the novel variation loss and MixUp regularization, which jointly leverage the limited positively labeled and large quantity of unlabeled text and, importantly, their interpolation from the BERT word embeddings. DALLMi also introduces a label-balanced sampling strategy to overcome the imbalance between labeled and unlabeled data. We evaluate DALLMi against the partial-supervised and unsupervised approach on three datasets under different scenarios of label availability for the target domain. Our results show that DALLMi achieves higher mAP than unsupervised and partially-supervised approaches by 19.9% and 52.2%, respectively. | [
"['Miruna Beţianu' 'Abele Mălan' 'Marco Aldinucci' 'Robert Birke'\n 'Lydia Chen']"
]
|
null | null | 2405.01906 | null | null | http://arxiv.org/pdf/2405.01906v1 | 2024-05-03T08:00:19Z | 2024-05-03T08:00:19Z | Instance-Conditioned Adaptation for Large-scale Generalization of Neural
Combinatorial Optimization | The neural combinatorial optimization (NCO) approach has shown great potential for solving routing problems without the requirement of expert knowledge. However, existing constructive NCO methods cannot directly solve large-scale instances, which significantly limits their application prospects. To address these crucial shortcomings, this work proposes a novel Instance-Conditioned Adaptation Model (ICAM) for better large-scale generalization of neural combinatorial optimization. In particular, we design a powerful yet lightweight instance-conditioned adaptation module for the NCO model to generate better solutions for instances across different scales. In addition, we develop an efficient three-stage reinforcement learning-based training scheme that enables the model to learn cross-scale features without any labeled optimal solution. Experimental results show that our proposed method is capable of obtaining excellent results with a very fast inference time in solving Traveling Salesman Problems (TSPs) and Capacitated Vehicle Routing Problems (CVRPs) across different scales. To the best of our knowledge, our model achieves state-of-the-art performance among all RL-based constructive methods for TSP and CVRP with up to 1,000 nodes. | [
"['Changliang Zhou' 'Xi Lin' 'Zhenkun Wang' 'Xialiang Tong' 'Mingxuan Yuan'\n 'Qingfu Zhang']"
]
|
null | null | 2405.01927 | null | null | http://arxiv.org/pdf/2405.01927v1 | 2024-05-03T08:44:04Z | 2024-05-03T08:44:04Z | SlotGAT: Slot-based Message Passing for Heterogeneous Graph Neural
Network | Heterogeneous graphs are ubiquitous to model complex data. There are urgent needs on powerful heterogeneous graph neural networks to effectively support important applications. We identify a potential semantic mixing issue in existing message passing processes, where the representations of the neighbors of a node $v$ are forced to be transformed to the feature space of $v$ for aggregation, though the neighbors are in different types. That is, the semantics in different node types are entangled together into node $v$'s representation. To address the issue, we propose SlotGAT with separate message passing processes in slots, one for each node type, to maintain the representations in their own node-type feature spaces. Moreover, in a slot-based message passing layer, we design an attention mechanism for effective slot-wise message aggregation. Further, we develop a slot attention technique after the last layer of SlotGAT, to learn the importance of different slots in downstream tasks. Our analysis indicates that the slots in SlotGAT can preserve different semantics in various feature spaces. The superiority of SlotGAT is evaluated against 13 baselines on 6 datasets for node classification and link prediction. Our code is at https://github.com/scottjiao/SlotGAT_ICML23/. | [
"['Ziang Zhou' 'Jieming Shi' 'Renchi Yang' 'Yuanhang Zou' 'Qing Li']"
]
|
null | null | 2405.01934 | null | null | http://arxiv.org/pdf/2405.01934v1 | 2024-05-03T08:58:38Z | 2024-05-03T08:58:38Z | Impact of Architectural Modifications on Deep Learning Adversarial
Robustness | Rapid advancements of deep learning are accelerating adoption in a wide variety of applications, including safety-critical applications such as self-driving vehicles, drones, robots, and surveillance systems. These advancements include applying variations of sophisticated techniques that improve the performance of models. However, such models are not immune to adversarial manipulations, which can cause the system to misbehave and remain unnoticed by experts. The frequency of modifications to existing deep learning models necessitates thorough analysis to determine the impact on models' robustness. In this work, we present an experimental evaluation of the effects of model modifications on deep learning model robustness using adversarial attacks. Our methodology involves examining the robustness of variations of models against various adversarial attacks. By conducting our experiments, we aim to shed light on the critical issue of maintaining the reliability and safety of deep learning models in safety- and security-critical applications. Our results indicate the pressing demand for an in-depth assessment of the effects of model changes on the robustness of models. | [
"['Firuz Juraev' 'Mohammed Abuhamad' 'Simon S. Woo'\n 'George K Thiruvathukal' 'Tamer Abuhmed']"
]
|
null | null | 2405.01943 | null | null | http://arxiv.org/pdf/2405.01943v2 | 2024-06-20T06:57:29Z | 2024-05-03T09:13:13Z | Dependency-Aware Semi-Structured Sparsity: Declining Roles of Outliers
in Pruning GLU-based LLMs | The rapid growth in the scale of Large Language Models (LLMs) has led to significant computational and memory costs, making model compression techniques such as network pruning increasingly crucial for their efficient deployment. Recent LLMs such as LLaMA2 and Mistral have adopted GLU-based MLP architectures. However, current LLM pruning strategies are primarily based on insights from older LLM architectures, necessitating a reevaluation of these strategies to suit the new architectural characteristics. Contrary to traditional beliefs, we find that outliers play a diminished role in the input projections of GLU-based MLPs. Leveraging this new insight, we propose Dependency-aware Semi-structured Sparsity (DaSS), a novel pruning method for GLU-based LLMs. DaSS balances the flexibility of unstructured pruning and the structural consistency of dependency-based structured pruning by considering both of weight magnitude and corresponding intermediate activation norms in weight pruning metric. Empirical evaluations on the Mistral, Gemma, and LLaMA2 model families demonstrate the consistent effectiveness of DaSS in the prevailing GLU variants. | [
"['Zhiyu Guo' 'Hidetaka Kamigaito' 'Taro Wanatnabe']"
]
|
null | null | 2405.01952 | null | null | http://arxiv.org/pdf/2405.01952v1 | 2024-05-03T09:27:31Z | 2024-05-03T09:27:31Z | Three Quantization Regimes for ReLU Networks | We establish the fundamental limits in the approximation of Lipschitz functions by deep ReLU neural networks with finite-precision weights. Specifically, three regimes, namely under-, over-, and proper quantization, in terms of minimax approximation error behavior as a function of network weight precision, are identified. This is accomplished by deriving nonasymptotic tight lower and upper bounds on the minimax approximation error. Notably, in the proper-quantization regime, neural networks exhibit memory-optimality in the approximation of Lipschitz functions. Deep networks have an inherent advantage over shallow networks in achieving memory-optimality. We also develop the notion of depth-precision tradeoff, showing that networks with high-precision weights can be converted into functionally equivalent deeper networks with low-precision weights, while preserving memory-optimality. This idea is reminiscent of sigma-delta analog-to-digital conversion, where oversampling rate is traded for resolution in the quantization of signal samples. We improve upon the best-known ReLU network approximation results for Lipschitz functions and describe a refinement of the bit extraction technique which could be of independent general interest. | [
"['Weigutian Ou' 'Philipp Schenkel' 'Helmut Bölcskei']"
]
|
null | null | 2405.01963 | null | null | http://arxiv.org/pdf/2405.01963v1 | 2024-05-03T09:40:47Z | 2024-05-03T09:40:47Z | From Attack to Defense: Insights into Deep Learning Security Measures in
Black-Box Settings | Deep Learning (DL) is rapidly maturing to the point that it can be used in safety- and security-crucial applications. However, adversarial samples, which are undetectable to the human eye, pose a serious threat that can cause the model to misbehave and compromise the performance of such applications. Addressing the robustness of DL models has become crucial to understanding and defending against adversarial attacks. In this study, we perform comprehensive experiments to examine the effect of adversarial attacks and defenses on various model architectures across well-known datasets. Our research focuses on black-box attacks such as SimBA, HopSkipJump, MGAAttack, and boundary attacks, as well as preprocessor-based defensive mechanisms, including bits squeezing, median smoothing, and JPEG filter. Experimenting with various models, our results demonstrate that the level of noise needed for the attack increases as the number of layers increases. Moreover, the attack success rate decreases as the number of layers increases. This indicates that model complexity and robustness have a significant relationship. Investigating the diversity and robustness relationship, our experiments with diverse models show that having a large number of parameters does not imply higher robustness. Our experiments extend to show the effects of the training dataset on model robustness. Using various datasets such as ImageNet-1000, CIFAR-100, and CIFAR-10 are used to evaluate the black-box attacks. Considering the multiple dimensions of our analysis, e.g., model complexity and training dataset, we examined the behavior of black-box attacks when models apply defenses. Our results show that applying defense strategies can significantly reduce attack effectiveness. This research provides in-depth analysis and insight into the robustness of DL models against various attacks, and defenses. | [
"['Firuz Juraev' 'Mohammed Abuhamad' 'Eric Chan-Tin'\n 'George K. Thiruvathukal' 'Tamer Abuhmed']"
]
|
null | null | 2405.01964 | null | null | http://arxiv.org/pdf/2405.01964v3 | 2024-06-17T12:48:46Z | 2024-05-03T09:41:39Z | Position: Understanding LLMs Requires More Than Statistical
Generalization | The last decade has seen blossoming research in deep learning theory attempting to answer, "Why does deep learning generalize?" A powerful shift in perspective precipitated this progress: the study of overparametrized models in the interpolation regime. In this paper, we argue that another perspective shift is due, since some of the desirable qualities of LLMs are not a consequence of good statistical generalization and require a separate theoretical explanation. Our core argument relies on the observation that AR probabilistic models are inherently non-identifiable: models zero or near-zero KL divergence apart -- thus, equivalent test loss -- can exhibit markedly different behaviors. We support our position with mathematical examples and empirical observations, illustrating why non-identifiability has practical relevance through three case studies: (1) the non-identifiability of zero-shot rule extrapolation; (2) the approximate non-identifiability of in-context learning; and (3) the non-identifiability of fine-tunability. We review promising research directions focusing on LLM-relevant generalization measures, transferability, and inductive biases. | [
"['Patrik Reizinger' 'Szilvia Ujváry' 'Anna Mészáros' 'Anna Kerekes'\n 'Wieland Brendel' 'Ferenc Huszár']"
]
|
null | null | 2405.01974 | null | null | http://arxiv.org/pdf/2405.01974v1 | 2024-05-03T09:57:44Z | 2024-05-03T09:57:44Z | Multitask Extension of Geometrically Aligned Transfer Encoder | Molecular datasets often suffer from a lack of data. It is well-known that gathering data is difficult due to the complexity of experimentation or simulation involved. Here, we leverage mutual information across different tasks in molecular data to address this issue. We extend an algorithm that utilizes the geometric characteristics of the encoding space, known as the Geometrically Aligned Transfer Encoder (GATE), to a multi-task setup. Thus, we connect multiple molecular tasks by aligning the curved coordinates onto locally flat coordinates, ensuring the flow of information from source tasks to support performance on target data. | [
"['Sung Moon Ko' 'Sumin Lee' 'Dae-Woong Jeong' 'Hyunseung Kim'\n 'Chanhui Lee' 'Soorin Yim' 'Sehui Han']"
]
|
null | null | 2405.01975 | null | null | http://arxiv.org/pdf/2405.01975v2 | 2024-05-07T11:28:02Z | 2024-05-03T10:00:36Z | Introducing a microstructure-embedded autoencoder approach for
reconstructing high-resolution solution field data from a reduced parametric
space | In this study, we develop a novel multi-fidelity deep learning approach that transforms low-fidelity solution maps into high-fidelity ones by incorporating parametric space information into a standard autoencoder architecture. This method's integration of parametric space information significantly reduces the need for training data to effectively predict high-fidelity solutions from low-fidelity ones. In this study, we examine a two-dimensional steady-state heat transfer analysis within a highly heterogeneous materials microstructure. The heat conductivity coefficients for two different materials are condensed from a 101 x 101 grid to smaller grids. We then solve the boundary value problem on the coarsest grid using a pre-trained physics-informed neural operator network known as Finite Operator Learning (FOL). The resulting low-fidelity solution is subsequently upscaled back to a 101 x 101 grid using a newly designed enhanced autoencoder. The novelty of the developed enhanced autoencoder lies in the concatenation of heat conductivity maps of different resolutions to the decoder segment in distinct steps. Hence the developed algorithm is named microstructure-embedded autoencoder (MEA). We compare the MEA outcomes with those from finite element methods, the standard U-Net, and various other upscaling techniques, including interpolation functions and feedforward neural networks (FFNN). Our analysis shows that MEA outperforms these methods in terms of computational efficiency and error on test cases. As a result, the MEA serves as a potential supplement to neural operator networks, effectively upscaling low-fidelity solutions to high fidelity while preserving critical details often lost in traditional upscaling methods, particularly at sharp interfaces like those seen with interpolation. | [
"['Rasoul Najafi Koopas' 'Shahed Rezaei' 'Natalie Rauter' 'Richard Ostwald'\n 'Rolf Lammering']"
]
|
null | null | 2405.01976 | null | null | http://arxiv.org/pdf/2405.01976v1 | 2024-05-03T10:00:45Z | 2024-05-03T10:00:45Z | Conformal Prediction for Natural Language Processing: A Survey | The rapid proliferation of large language models and natural language processing (NLP) applications creates a crucial need for uncertainty quantification to mitigate risks such as hallucinations and to enhance decision-making reliability in critical applications. Conformal prediction is emerging as a theoretically sound and practically useful framework, combining flexibility with strong statistical guarantees. Its model-agnostic and distribution-free nature makes it particularly promising to address the current shortcomings of NLP systems that stem from the absence of uncertainty quantification. This paper provides a comprehensive survey of conformal prediction techniques, their guarantees, and existing applications in NLP, pointing to directions for future research and open challenges. | [
"['Margarida M. Campos' 'António Farinhas' 'Chrysoula Zerva'\n 'Mário A. T. Figueiredo' 'André F. T. Martins']"
]
|
null | null | 2405.01978 | null | null | http://arxiv.org/pdf/2405.01978v1 | 2024-05-03T10:05:31Z | 2024-05-03T10:05:31Z | Quantifying Distribution Shifts and Uncertainties for Enhanced Model
Robustness in Machine Learning Applications | Distribution shifts, where statistical properties differ between training and test datasets, present a significant challenge in real-world machine learning applications where they directly impact model generalization and robustness. In this study, we explore model adaptation and generalization by utilizing synthetic data to systematically address distributional disparities. Our investigation aims to identify the prerequisites for successful model adaptation across diverse data distributions, while quantifying the associated uncertainties. Specifically, we generate synthetic data using the Van der Waals equation for gases and employ quantitative measures such as Kullback-Leibler divergence, Jensen-Shannon distance, and Mahalanobis distance to assess data similarity. These metrics en able us to evaluate both model accuracy and quantify the associated uncertainty in predictions arising from data distribution shifts. Our findings suggest that utilizing statistical measures, such as the Mahalanobis distance, to determine whether model predictions fall within the low-error "interpolation regime" or the high-error "extrapolation regime" provides a complementary method for assessing distribution shift and model uncertainty. These insights hold significant value for enhancing model robustness and generalization, essential for the successful deployment of machine learning applications in real-world scenarios. | [
"['Vegard Flovik']"
]
|
null | null | 2405.01988 | null | null | http://arxiv.org/pdf/2405.01988v1 | 2024-05-03T10:42:17Z | 2024-05-03T10:42:17Z | Joint sentiment analysis of lyrics and audio in music | Sentiment or mood can express themselves on various levels in music. In automatic analysis, the actual audio data is usually analyzed, but the lyrics can also play a crucial role in the perception of moods. We first evaluate various models for sentiment analysis based on lyrics and audio separately. The corresponding approaches already show satisfactory results, but they also exhibit weaknesses, the causes of which we examine in more detail. Furthermore, different approaches to combining the audio and lyrics results are proposed and evaluated. Considering both modalities generally leads to improved performance. We investigate misclassifications and (also intentional) contradictions between audio and lyrics sentiment more closely, and identify possible causes. Finally, we address fundamental problems in this research area, such as high subjectivity, lack of data, and inconsistency in emotion taxonomies. | [
"['Lea Schaab' 'Anna Kruspe']"
]
|
null | null | 2405.01990 | null | null | http://arxiv.org/pdf/2405.01990v1 | 2024-05-03T10:46:19Z | 2024-05-03T10:46:19Z | Soft Label PU Learning | PU learning refers to the classification problem in which only part of positive samples are labeled. Existing PU learning methods treat unlabeled samples equally. However, in many real tasks, from common sense or domain knowledge, some unlabeled samples are more likely to be positive than others. In this paper, we propose soft label PU learning, in which unlabeled data are assigned soft labels according to their probabilities of being positive. Considering that the ground truth of TPR, FPR, and AUC are unknown, we then design PU counterparts of these metrics to evaluate the performances of soft label PU learning methods within validation data. We show that these new designed PU metrics are good substitutes for the real metrics. After that, a method that optimizes such metrics is proposed. Experiments on public datasets and real datasets for anti-cheat services from Tencent games demonstrate the effectiveness of our proposed method. | [
"['Puning Zhao' 'Jintao Deng' 'Xu Cheng']"
]
|
null | null | 2405.01994 | null | null | http://arxiv.org/pdf/2405.01994v1 | 2024-05-03T10:50:30Z | 2024-05-03T10:50:30Z | Mathematics of statistical sequential decision-making: concentration,
risk-awareness and modelling in stochastic bandits, with applications to
bariatric surgery | This thesis aims to study some of the mathematical challenges that arise in the analysis of statistical sequential decision-making algorithms for postoperative patients follow-up. Stochastic bandits (multiarmed, contextual) model the learning of a sequence of actions (policy) by an agent in an uncertain environment in order to maximise observed rewards. To learn optimal policies, bandit algorithms have to balance the exploitation of current knowledge and the exploration of uncertain actions. Such algorithms have largely been studied and deployed in industrial applications with large datasets, low-risk decisions and clear modelling assumptions, such as clickthrough rate maximisation in online advertising. By contrast, digital health recommendations call for a whole new paradigm of small samples, risk-averse agents and complex, nonparametric modelling. To this end, we developed new safe, anytime-valid concentration bounds, (Bregman, empirical Chernoff), introduced a new framework for risk-aware contextual bandits (with elicitable risk measures) and analysed a novel class of nonparametric bandit algorithms under weak assumptions (Dirichlet sampling). In addition to the theoretical guarantees, these results are supported by in-depth empirical evidence. Finally, as a first step towards personalised postoperative follow-up recommendations, we developed with medical doctors and surgeons an interpretable machine learning model to predict the long-term weight trajectories of patients after bariatric surgery. | [
"['Patrick Saux']"
]
|
null | null | 2405.01995 | null | null | http://arxiv.org/abs/2405.01995v1 | 2024-05-03T10:50:30Z | 2024-05-03T10:50:30Z | Cooperation and Federation in Distributed Radar Point Cloud Processing | The paper considers the problem of human-scale RF sensing utilizing a network of resource-constrained MIMO radars with low range-azimuth resolution. The radars operate in the mmWave band and obtain time-varying 3D point cloud (PC) information that is sensitive to body movements. They also observe the same scene from different views and cooperate while sensing the environment using a sidelink communication channel. Conventional cooperation setups allow the radars to mutually exchange raw PC information to improve ego sensing. The paper proposes a federation mechanism where the radars exchange the parameters of a Bayesian posterior measure of the observed PCs, rather than raw data. The radars act as distributed parameter servers to reconstruct a global posterior (i.e., federated posterior) using Bayesian tools. The paper quantifies and compares the benefits of radar federation with respect to cooperation mechanisms. Both approaches are validated by experiments with a real-time demonstration platform. Federation makes minimal use of the sidelink communication channel (20 {div} 25 times lower bandwidth use) and is less sensitive to unresolved targets. On the other hand, cooperation reduces the mean absolute target estimation error of about 20%. | [
"['S. Savazzi' 'V. Rampa' 'S. Kianoush' 'A. Minora' 'L. Costa']"
]
|
null | null | 2405.02041 | null | null | http://arxiv.org/pdf/2405.02041v1 | 2024-05-03T12:20:08Z | 2024-05-03T12:20:08Z | Stabilizing Backpropagation Through Time to Learn Complex Physics | Of all the vector fields surrounding the minima of recurrent learning setups, the gradient field with its exploding and vanishing updates appears a poor choice for optimization, offering little beyond efficient computability. We seek to improve this suboptimal practice in the context of physics simulations, where backpropagating feedback through many unrolled time steps is considered crucial to acquiring temporally coherent behavior. The alternative vector field we propose follows from two principles: physics simulators, unlike neural networks, have a balanced gradient flow, and certain modifications to the backpropagation pass leave the positions of the original minima unchanged. As any modification of backpropagation decouples forward and backward pass, the rotation-free character of the gradient field is lost. Therefore, we discuss the negative implications of using such a rotational vector field for optimization and how to counteract them. Our final procedure is easily implementable via a sequence of gradient stopping and component-wise comparison operations, which do not negatively affect scalability. Our experiments on three control problems show that especially as we increase the complexity of each task, the unbalanced updates from the gradient can no longer provide the precise control signals necessary while our method still solves the tasks. Our code can be found at https://github.com/tum-pbs/StableBPTT. | [
"['Patrick Schnell' 'Nils Thuerey']"
]
|
null | null | 2405.02044 | null | null | http://arxiv.org/pdf/2405.02044v1 | 2024-05-03T12:21:43Z | 2024-05-03T12:21:43Z | Zero-Sum Positional Differential Games as a Framework for Robust
Reinforcement Learning: Deep Q-Learning Approach | Robust Reinforcement Learning (RRL) is a promising Reinforcement Learning (RL) paradigm aimed at training robust to uncertainty or disturbances models, making them more efficient for real-world applications. Following this paradigm, uncertainty or disturbances are interpreted as actions of a second adversarial agent, and thus, the problem is reduced to seeking the agents' policies robust to any opponent's actions. This paper is the first to propose considering the RRL problems within the positional differential game theory, which helps us to obtain theoretically justified intuition to develop a centralized Q-learning approach. Namely, we prove that under Isaacs's condition (sufficiently general for real-world dynamical systems), the same Q-function can be utilized as an approximate solution of both minimax and maximin Bellman equations. Based on these results, we present the Isaacs Deep Q-Network algorithms and demonstrate their superiority compared to other baseline RRL and Multi-Agent RL algorithms in various environments. | [
"['Anton Plaksin' 'Vitaly Kalev']"
]
|
null | null | 2405.02060 | null | null | http://arxiv.org/abs/2405.02060v1 | 2024-05-03T12:42:40Z | 2024-05-03T12:42:40Z | Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case | In this paper, we show how Federated Learning (FL) can be applied to vehicular use-cases in which we seek to classify obstacles, irregularities and pavement types on roads. Our proposed framework utilizes FL and TabNet, a state-of-the-art neural network for tabular data. We are the first to demonstrate how TabNet can be integrated with FL. Moreover, we achieve a maximum test accuracy of 93.6%. Finally, we reason why FL is a suitable concept for this data set. | [
"['William Lindskog' 'Christian Prehofer']"
]
|
null | null | 2405.02062 | null | null | http://arxiv.org/pdf/2405.02062v1 | 2024-05-03T12:44:52Z | 2024-05-03T12:44:52Z | Dyna-Style Learning with A Macroscopic Model for Vehicle Platooning in
Mixed-Autonomy Traffic | Platooning of connected and autonomous vehicles (CAVs) plays a vital role in modernizing highways, ushering in enhanced efficiency and safety. This paper explores the significance of platooning in smart highways, employing a coupled partial differential equation (PDE) and ordinary differential equation (ODE) model to elucidate the complex interaction between bulk traffic flow and CAV platoons. Our study focuses on developing a Dyna-style planning and learning framework tailored for platoon control, with a specific goal of reducing fuel consumption. By harnessing the coupled PDE-ODE model, we improve data efficiency in Dyna-style learning through virtual experiences. Simulation results validate the effectiveness of our macroscopic model in modeling platoons within mixed-autonomy settings, demonstrating a notable $10.11%$ reduction in vehicular fuel consumption compared to conventional approaches. | [
"['Yichuan Zou' 'Li Jin' 'Xi Xiong']"
]
|
null | null | 2405.02063 | null | null | http://arxiv.org/pdf/2405.02063v2 | 2024-05-21T15:58:11Z | 2024-05-03T12:48:21Z | Few-sample Variational Inference of Bayesian Neural Networks with
Arbitrary Nonlinearities | Bayesian Neural Networks (BNNs) extend traditional neural networks to provide uncertainties associated with their outputs. On the forward pass through a BNN, predictions (and their uncertainties) are made either by Monte Carlo sampling network weights from the learned posterior or by analytically propagating statistical moments through the network. Though flexible, Monte Carlo sampling is computationally expensive and can be infeasible or impractical under resource constraints or for large networks. While moment propagation can ameliorate the computational costs of BNN inference, it can be difficult or impossible for networks with arbitrary nonlinearities, thereby restricting the possible set of network layers permitted with such a scheme. In this work, we demonstrate a simple yet effective approach for propagating statistical moments through arbitrary nonlinearities with only 3 deterministic samples, enabling few-sample variational inference of BNNs without restricting the set of network layers used. Furthermore, we leverage this approach to demonstrate a novel nonlinear activation function that we use to inject physics-informed prior information into output nodes of a BNN. | [
"['David J. Schodt']"
]
|
null | null | 2405.02067 | null | null | http://arxiv.org/abs/2405.02067v1 | 2024-05-03T12:58:57Z | 2024-05-03T12:58:57Z | Histogram-Based Federated XGBoost using Minimal Variance Sampling for
Federated Tabular Data | Federated Learning (FL) has gained considerable traction, yet, for tabular data, FL has received less attention. Most FL research has focused on Neural Networks while Tree-Based Models (TBMs) such as XGBoost have historically performed better on tabular data. It has been shown that subsampling of training data when building trees can improve performance but it is an open problem whether such subsampling can improve performance in FL. In this paper, we evaluate a histogram-based federated XGBoost that uses Minimal Variance Sampling (MVS). We demonstrate the underlying algorithm and show that our model using MVS can improve performance in terms of accuracy and regression error in a federated setting. In our evaluation, our model using MVS performs better than uniform (random) sampling and no sampling at all. It achieves both outstanding local and global performance on a new set of federated tabular datasets. Federated XGBoost using MVS also outperforms centralized XGBoost in half of the studied cases. | [
"['William Lindskog' 'Christian Prehofer' 'Sarandeep Singh']"
]
|
null | null | 2405.02074 | null | null | http://arxiv.org/abs/2405.02074v1 | 2024-05-03T13:08:56Z | 2024-05-03T13:08:56Z | A Federated Learning Benchmark on Tabular Data: Comparing Tree-Based
Models and Neural Networks | Federated Learning (FL) has lately gained traction as it addresses how machine learning models train on distributed datasets. FL was designed for parametric models, namely Deep Neural Networks (DNNs).Thus, it has shown promise on image and text tasks. However, FL for tabular data has received little attention. Tree-Based Models (TBMs) have been considered to perform better on tabular data and they are starting to see FL integrations. In this study, we benchmark federated TBMs and DNNs for horizontal FL, with varying data partitions, on 10 well-known tabular datasets. Our novel benchmark results indicates that current federated boosted TBMs perform better than federated DNNs in different data partitions. Furthermore, a federated XGBoost outperforms all other models. Lastly, we find that federated TBMs perform better than federated parametric models, even when increasing the number of clients significantly. | [
"['William Lindskog' 'Christian Prehofer']"
]
|
null | null | 2405.02081 | null | null | http://arxiv.org/pdf/2405.02081v1 | 2024-05-03T13:15:29Z | 2024-05-03T13:15:29Z | A Mutual Information Perspective on Federated Contrastive Learning | We investigate contrastive learning in the federated setting through the lens of SimCLR and multi-view mutual information maximization. In doing so, we uncover a connection between contrastive representation learning and user verification; by adding a user verification loss to each client's local SimCLR loss we recover a lower bound to the global multi-view mutual information. To accommodate for the case of when some labelled data are available at the clients, we extend our SimCLR variant to the federated semi-supervised setting. We see that a supervised SimCLR objective can be obtained with two changes: a) the contrastive loss is computed between datapoints that share the same label and b) we require an additional auxiliary head that predicts the correct labels from either of the two views. Along with the proposed SimCLR extensions, we also study how different sources of non-i.i.d.-ness can impact the performance of federated unsupervised learning through global mutual information maximization; we find that a global objective is beneficial for some sources of non-i.i.d.-ness but can be detrimental for others. We empirically evaluate our proposed extensions in various tasks to validate our claims and furthermore demonstrate that our proposed modifications generalize to other pretraining methods. | [
"['Christos Louizos' 'Matthias Reisser' 'Denis Korzhenkov']"
]
|
null | null | 2405.02082 | null | null | http://arxiv.org/pdf/2405.02082v1 | 2024-05-03T13:19:33Z | 2024-05-03T13:19:33Z | A comparative study of conformal prediction methods for valid
uncertainty quantification in machine learning | In the past decades, most work in the area of data analysis and machine learning was focused on optimizing predictive models and getting better results than what was possible with existing models. To what extent the metrics with which such improvements were measured were accurately capturing the intended goal, whether the numerical differences in the resulting values were significant, or whether uncertainty played a role in this study and if it should have been taken into account, was of secondary importance. Whereas probability theory, be it frequentist or Bayesian, used to be the gold standard in science before the advent of the supercomputer, it was quickly replaced in favor of black box models and sheer computing power because of their ability to handle large data sets. This evolution sadly happened at the expense of interpretability and trustworthiness. However, while people are still trying to improve the predictive power of their models, the community is starting to realize that for many applications it is not so much the exact prediction that is of importance, but rather the variability or uncertainty. The work in this dissertation tries to further the quest for a world where everyone is aware of uncertainty, of how important it is and how to embrace it instead of fearing it. A specific, though general, framework that allows anyone to obtain accurate uncertainty estimates is singled out and analysed. Certain aspects and applications of the framework -- dubbed `conformal prediction' -- are studied in detail. Whereas many approaches to uncertainty quantification make strong assumptions about the data, conformal prediction is, at the time of writing, the only framework that deserves the title `distribution-free'. No parametric assumptions have to be made and the nonparametric results also hold without having to resort to the law of large numbers in the asymptotic regime. | [
"['Nicolas Dewolf']"
]
|
null | null | 2405.02086 | null | null | http://arxiv.org/pdf/2405.02086v2 | 2024-07-04T07:58:17Z | 2024-05-03T13:21:49Z | Multi-level projection with exponential parallel speedup; Application to
sparse auto-encoders neural networks | The $ell_{1,infty}$ norm is an efficient structured projection but the complexity of the best algorithm is unfortunately $mathcal{O}big(n m log(n m)big)$ for a matrix in $mathbb{R}^{ntimes m}$. In this paper, we propose a new bi-level projection method for which we show that the time complexity for the $ell_{1,infty}$ norm is only $mathcal{O}big(n m big)$ for a matrix in $mathbb{R}^{ntimes m}$, and $mathcal{O}big(n + m big)$ with full parallel power. We generalize our method to tensors and we propose a new multi-level projection, having an induced decomposition that yields a linear parallel speedup up to an exponential speedup factor, resulting in a time complexity lower-bounded by the sum of the dimensions, instead of the product of the dimensions. we provide a large base of implementation of our framework for bi-level and tri-level (matrices and tensors) for various norms and provides also the parallel implementation. Experiments show that our projection is $2$ times faster than the actual fastest Euclidean algorithms while providing same accuracy and better sparsity in neural networks applications. | [
"['Guillaume Perez' 'Michel Barlaud']"
]
|
null | null | 2405.02098 | null | null | http://arxiv.org/pdf/2405.02098v3 | 2024-05-09T12:19:09Z | 2024-05-03T13:48:05Z | Forecasting Ferry Passenger Flow Using Long-Short Term Memory Neural
Networks | With recent studies related to Neural Networks being used on different forecasting and time series investigations, this study aims to expand these contexts to ferry passenger traffic. The primary objective of the study is to investigate and evaluate an LSTM-based Neural Networks' capability to forecast ferry passengers of two ports in the Philippines. The proposed model's fitting and evaluation of the passenger flow forecasting of the two ports is based on monthly passenger traffic from 2016 to 2022 data that was acquired from the Philippine Ports Authority (PPA). This work uses Mean Absolute Percentage Error (MAPE) as its primary metric to evaluate the model's forecasting capability. The proposed LSTM-based Neural Networks model achieved 72% forecasting accuracy to the Batangas port ferry passenger data and 74% forecasting accuracy to the Mindoro port ferry passenger data. Using Keras and Scikit-learn Python libraries, this work concludes a reasonable forecasting performance of the presented LSTM model. Aside from these notable findings, this study also recommends further investigation and studies on employing other statistical, machine learning, and deep learning methods on forecasting ferry passenger flows. | [
"['Daniel Fesalbon']"
]
|
null | null | 2405.02101 | null | null | http://arxiv.org/pdf/2405.02101v1 | 2024-05-03T13:54:59Z | 2024-05-03T13:54:59Z | Discrete Aware Matrix Completion via Convexized $\ell_0$-Norm
Approximation | We consider a novel algorithm, for the completion of partially observed low-rank matrices in a structured setting where each entry can be chosen from a finite discrete alphabet set, such as in common recommender systems. The proposed low-rank matrix completion (MC) method is an improved variation of state-of-the-art (SotA) discrete aware matrix completion method which we previously proposed, in which discreteness is enforced by an $ell_0$-norm regularizer, not by replaced with the $ell_1$-norm, but instead approximated by a continuous and differentiable function normalized via fractional programming (FP) under a proximal gradient (PG) framework. Simulation results demonstrate the superior performance of the new method compared to the SotA techniques as well as the earlier $ell_1$-norm-based discrete-aware matrix completion approach. | [
"['Niclas Führling' 'Kengo Ando' 'Giuseppe Thadeu Freitas de Abreu'\n 'David González G.' 'Osvaldo Gonsa']"
]
|
null | null | 2405.02119 | null | null | http://arxiv.org/pdf/2405.02119v1 | 2024-05-03T14:19:40Z | 2024-05-03T14:19:40Z | Can We Identify Unknown Audio Recording Environments in Forensic
Scenarios? | Audio recordings may provide important evidence in criminal investigations. One such case is the forensic association of the recorded audio to the recording location. For example, a voice message may be the only investigative cue to narrow down the candidate sites for a crime. Up to now, several works provide tools for closed-set recording environment classification under relatively clean recording conditions. However, in forensic investigations, the candidate locations are case-specific. Thus, closed-set tools are not applicable without retraining on a sufficient amount of training samples for each case and respective candidate set. In addition, a forensic tool has to deal with audio material from uncontrolled sources with variable properties and quality. In this work, we therefore attempt a major step towards practical forensic application scenarios. We propose a representation learning framework called EnvId, short for environment identification. EnvId avoids case-specific retraining. Instead, it is the first tool for robust few-shot classification of unseen environment locations. We demonstrate that EnvId can handle forensically challenging material. It provides good quality predictions even under unseen signal degradations, environment characteristics or recording position mismatches. Our code and datasets will be made publicly available upon acceptance. | [
"['Denise Moussa' 'Germans Hirsch' 'Christian Riess']"
]
|
null | null | 2405.02124 | null | null | http://arxiv.org/pdf/2405.02124v1 | 2024-05-03T14:25:21Z | 2024-05-03T14:25:21Z | TIPAA-SSL: Text Independent Phone-to-Audio Alignment based on
Self-Supervised Learning and Knowledge Transfer | In this paper, we present a novel approach for text independent phone-to-audio alignment based on phoneme recognition, representation learning and knowledge transfer. Our method leverages a self-supervised model (wav2vec2) fine-tuned for phoneme recognition using a Connectionist Temporal Classification (CTC) loss, a dimension reduction model and a frame-level phoneme classifier trained thanks to forced-alignment labels (using Montreal Forced Aligner) to produce multi-lingual phonetic representations, thus requiring minimal additional training. We evaluate our model using synthetic native data from the TIMIT dataset and the SCRIBE dataset for American and British English, respectively. Our proposed model outperforms the state-of-the-art (charsiu) in statistical metrics and has applications in language learning and speech processing systems. We leave experiments on other languages for future work but the design of the system makes it easily adaptable to other languages. | [
"['Noé Tits' 'Prernna Bhatnagar' 'Thierry Dutoit']"
]
|
null | null | 2405.02140 | null | null | http://arxiv.org/pdf/2405.02140v2 | 2024-06-26T14:58:25Z | 2024-05-03T14:43:07Z | An Information Theoretic Perspective on Conformal Prediction | Conformal Prediction (CP) is a distribution-free uncertainty estimation framework that constructs prediction sets guaranteed to contain the true answer with a user-specified probability. Intuitively, the size of the prediction set encodes a general notion of uncertainty, with larger sets associated with higher degrees of uncertainty. In this work, we leverage information theory to connect conformal prediction to other notions of uncertainty. More precisely, we prove three different ways to upper bound the intrinsic uncertainty, as described by the conditional entropy of the target variable given the inputs, by combining CP with information theoretical inequalities. Moreover, we demonstrate two direct and useful applications of such connection between conformal prediction and information theory: (i) more principled and effective conformal training objectives that generalize previous approaches and enable end-to-end training of machine learning models from scratch, and (ii) a natural mechanism to incorporate side information into conformal prediction. We empirically validate both applications in centralized and federated learning settings, showing our theoretical results translate to lower inefficiency (average prediction set size) for popular CP methods. | [
"['Alvaro H. C. Correia' 'Fabio Valerio Massoli' 'Christos Louizos'\n 'Arash Behboodi']"
]
|
null | null | 2405.02141 | null | null | http://arxiv.org/pdf/2405.02141v1 | 2024-05-03T14:44:04Z | 2024-05-03T14:44:04Z | Multi-Objective Recommendation via Multivariate Policy Learning | Real-world recommender systems often need to balance multiple objectives when deciding which recommendations to present to users. These include behavioural signals (e.g. clicks, shares, dwell time), as well as broader objectives (e.g. diversity, fairness). Scalarisation methods are commonly used to handle this balancing task, where a weighted average of per-objective reward signals determines the final score used for ranking. Naturally, how these weights are computed exactly, is key to success for any online platform. We frame this as a decision-making task, where the scalarisation weights are actions taken to maximise an overall North Star reward (e.g. long-term user retention or growth). We extend existing policy learning methods to the continuous multivariate action domain, proposing to maximise a pessimistic lower bound on the North Star reward that the learnt policy will yield. Typical lower bounds based on normal approximations suffer from insufficient coverage, and we propose an efficient and effective policy-dependent correction for this. We provide guidance to design stochastic data collection policies, as well as highly sensitive reward signals. Empirical observations from simulations, offline and online experiments highlight the efficacy of our deployed approach. | [
"['Olivier Jeunen' 'Jatin Mandav' 'Ivan Potapov' 'Nakul Agarwal'\n 'Sourabh Vaid' 'Wenzhe Shi' 'Aleksei Ustimenko']"
]
|
null | null | 2405.02148 | null | null | http://arxiv.org/pdf/2405.02148v1 | 2024-05-03T14:53:46Z | 2024-05-03T14:53:46Z | Towards a Formal Creativity Theory: Preliminary results in Novelty and
Transformativeness | Formalizing creativity-related concepts has been a long-term goal of Computational Creativity. To the same end, we explore Formal Learning Theory in the context of creativity. We provide an introduction to the main concepts of this framework and a re-interpretation of terms commonly found in creativity discussions, proposing formal definitions for novelty and transformational creativity. This formalisation marks the beginning of a research branch we call Formal Creativity Theory, exploring how learning can be included as preparation for exploratory behaviour and how learning is a key part of transformational creative behaviour. By employing these definitions, we argue that, while novelty is neither necessary nor sufficient for transformational creativity in general, when using an inspiring set, rather than a sequence of experiences, an agent actually requires novelty for transformational creativity to occur. | [
"['Luís Espírito Santo' 'Geraint Wiggins' 'Amílcar Cardoso']"
]
|
null | null | 2405.02154 | null | null | http://arxiv.org/pdf/2405.02154v2 | 2024-07-08T18:38:41Z | 2024-05-03T15:02:21Z | Neural Context Flows for Learning Generalizable Dynamical Systems | Neural Ordinary Differential Equations typically struggle to generalize to new dynamical behaviors created by parameter changes in the underlying system, even when the dynamics are close to previously seen behaviors. The issue gets worse when the changing parameters are unobserved, i.e., their value or influence is not directly measurable when collecting data. We introduce Neural Context Flow (NCF), a framework that encodes said unobserved parameters in a latent context vector as input to a vector field. NCFs leverage differentiability of the vector field with respect to the parameters, along with first-order Taylor expansion to allow any context vector to influence trajectories from other parameters. We validate our method and compare it to established Multi-Task and Meta-Learning alternatives, showing competitive performance in mean squared error for in-domain and out-of-distribution evaluation on the Lotka-Volterra, Glycolytic Oscillator, and Gray-Scott problems. This study holds practical implications for foundational models in science and related areas that benefit from conditional neural ODEs. Our code is openly available at https://github.com/ddrous/ncflow. | [
"['Roussel Desmond Nzoyem' 'David A. W. Barton' 'Tom Deakin']"
]
|
null | null | 2405.02161 | null | null | http://arxiv.org/pdf/2405.02161v1 | 2024-05-03T15:08:25Z | 2024-05-03T15:08:25Z | Simulating the economic impact of rationality through reinforcement
learning and agent-based modelling | Agent-based models (ABMs) are simulation models used in economics to overcome some of the limitations of traditional frameworks based on general equilibrium assumptions. However, agents within an ABM follow predetermined, not fully rational, behavioural rules which can be cumbersome to design and difficult to justify. Here we leverage multi-agent reinforcement learning (RL) to expand the capabilities of ABMs with the introduction of fully rational agents that learn their policy by interacting with the environment and maximising a reward function. Specifically, we propose a 'Rational macro ABM' (R-MABM) framework by extending a paradigmatic macro ABM from the economic literature. We show that gradually substituting ABM firms in the model with RL agents, trained to maximise profits, allows for a thorough study of the impact of rationality on the economy. We find that RL agents spontaneously learn three distinct strategies for maximising profits, with the optimal strategy depending on the level of market competition and rationality. We also find that RL agents with independent policies, and without the ability to communicate with each other, spontaneously learn to segregate into different strategic groups, thus increasing market power and overall profits. Finally, we find that a higher degree of rationality in the economy always improves the macroeconomic environment as measured by total output, depending on the specific rational policy, this can come at the cost of higher instability. Our R-MABM framework is general, it allows for stable multi-agent learning, and represents a principled and robust direction to extend existing economic simulators. | [
"['Simone Brusatin' 'Tommaso Padoan' 'Andrea Coletta'\n 'Domenico Delli Gatti' 'Aldo Glielmo']"
]
|
null | null | 2405.02175 | null | null | http://arxiv.org/pdf/2405.02175v2 | 2024-05-15T17:56:25Z | 2024-05-03T15:25:48Z | Hoaxpedia: A Unified Wikipedia Hoax Articles Dataset | Hoaxes are a recognised form of disinformation created deliberately, with potential serious implications in the credibility of reference knowledge resources such as Wikipedia. What makes detecting Wikipedia hoaxes hard is that they often are written according to the official style guidelines. In this work, we first provide a systematic analysis of the similarities and discrepancies between legitimate and hoax Wikipedia articles, and introduce Hoaxpedia, a collection of 311 Hoax articles (from existing literature as well as official Wikipedia lists) alongside semantically similar real articles. We report results of binary classification experiments in the task of predicting whether a Wikipedia article is real or hoax, and analyze several settings as well as a range of language models. Our results suggest that detecting deceitful content in Wikipedia based on content alone, despite not having been explored much in the past, is a promising direction. | [
"['Hsuvas Borkakoty' 'Luis Espinosa-Anke']"
]
|
null | null | 2405.02180 | null | null | http://arxiv.org/pdf/2405.02180v3 | 2024-05-09T12:47:01Z | 2024-05-03T15:27:51Z | A Flow-Based Model for Conditional and Probabilistic Electricity
Consumption Profile Generation and Prediction | Residential Load Profile (RLP) generation and prediction are critical for the operation and planning of distribution networks, especially as diverse low-carbon technologies (e.g., photovoltaic and electric vehicles) are increasingly adopted. This paper introduces a novel flow-based generative model, termed Full Convolutional Profile Flow (FCPFlow), which is uniquely designed for both conditional and unconditional RLP generation, and for probabilistic load forecasting. By introducing two new layers--the invertible linear layer and the invertible normalization layer--the proposed FCPFlow architecture shows three main advantages compared to traditional statistical and contemporary deep generative models: 1) it is well-suited for RLP generation under continuous conditions, such as varying weather and annual electricity consumption, 2) it demonstrates superior scalability in different datasets compared to traditional statistical models, and 3) it also demonstrates better modeling capabilities in capturing the complex correlation of RLPs compared with deep generative models. | [
"['Weijie Xia' 'Chenguang Wang' 'Peter Palensky' 'Pedro P. Vergara']"
]
|
null | null | 2405.02181 | null | null | http://arxiv.org/pdf/2405.02181v1 | 2024-05-03T15:28:44Z | 2024-05-03T15:28:44Z | Imitation Learning in Discounted Linear MDPs without exploration
assumptions | We present a new algorithm for imitation learning in infinite horizon linear MDPs dubbed ILARL which greatly improves the bound on the number of trajectories that the learner needs to sample from the environment. In particular, we remove exploration assumptions required in previous works and we improve the dependence on the desired accuracy $epsilon$ from $mathcal{O}br{epsilon^{-5}}$ to $mathcal{O}br{epsilon^{-4}}$. Our result relies on a connection between imitation learning and online learning in MDPs with adversarial losses. For the latter setting, we present the first result for infinite horizon linear MDP which may be of independent interest. Moreover, we are able to provide a strengthen result for the finite horizon case where we achieve $mathcal{O}br{epsilon^{-2}}$. Numerical experiments with linear function approximation shows that ILARL outperforms other commonly used algorithms. | [
"['Luca Viano' 'Stratis Skoulakis' 'Volkan Cevher']"
]
|
null | null | 2405.02183 | null | null | http://arxiv.org/pdf/2405.02183v1 | 2024-05-03T15:31:18Z | 2024-05-03T15:31:18Z | Metalearners for Ranking Treatment Effects | Efficiently allocating treatments with a budget constraint constitutes an important challenge across various domains. In marketing, for example, the use of promotions to target potential customers and boost conversions is limited by the available budget. While much research focuses on estimating causal effects, there is relatively limited work on learning to allocate treatments while considering the operational context. Existing methods for uplift modeling or causal inference primarily estimate treatment effects, without considering how this relates to a profit maximizing allocation policy that respects budget constraints. The potential downside of using these methods is that the resulting predictive model is not aligned with the operational context. Therefore, prediction errors are propagated to the optimization of the budget allocation problem, subsequently leading to a suboptimal allocation policy. We propose an alternative approach based on learning to rank. Our proposed methodology directly learns an allocation policy by prioritizing instances in terms of their incremental profit. We propose an efficient sampling procedure for the optimization of the ranking model to scale our methodology to large-scale data sets. Theoretically, we show how learning to rank can maximize the area under a policy's incremental profit curve. Empirically, we validate our methodology and show its effectiveness in practice through a series of experiments on both synthetic and real-world data. | [
"['Toon Vanderschueren' 'Wouter Verbeke' 'Felipe Moraes'\n 'Hugo Manuel Proença']"
]
|
null | null | 2405.02188 | null | null | http://arxiv.org/pdf/2405.02188v1 | 2024-05-03T15:44:31Z | 2024-05-03T15:44:31Z | Optimistic Regret Bounds for Online Learning in Adversarial Markov
Decision Processes | The Adversarial Markov Decision Process (AMDP) is a learning framework that deals with unknown and varying tasks in decision-making applications like robotics and recommendation systems. A major limitation of the AMDP formalism, however, is pessimistic regret analysis results in the sense that although the cost function can change from one episode to the next, the evolution in many settings is not adversarial. To address this, we introduce and study a new variant of AMDP, which aims to minimize regret while utilizing a set of cost predictors. For this setting, we develop a new policy search method that achieves a sublinear optimistic regret with high probability, that is a regret bound which gracefully degrades with the estimation power of the cost predictors. Establishing such optimistic regret bounds is nontrivial given that (i) as we demonstrate, the existing importance-weighted cost estimators cannot establish optimistic bounds, and (ii) the feedback model of AMDP is different (and more realistic) than the existing optimistic online learning works. Our result, in particular, hinges upon developing a novel optimistically biased cost estimator that leverages cost predictors and enables a high-probability regret analysis without imposing restrictive assumptions. We further discuss practical extensions of the proposed scheme and demonstrate its efficacy numerically. | [
"['Sang Bin Moon' 'Abolfazl Hashemi']"
]
|
null | null | 2405.02191 | null | null | http://arxiv.org/pdf/2405.02191v1 | 2024-05-03T15:47:07Z | 2024-05-03T15:47:07Z | Non-Destructive Peat Analysis using Hyperspectral Imaging and Machine
Learning | Peat, a crucial component in whisky production, imparts distinctive and irreplaceable flavours to the final product. However, the extraction of peat disrupts ancient ecosystems and releases significant amounts of carbon, contributing to climate change. This paper aims to address this issue by conducting a feasibility study on enhancing peat use efficiency in whisky manufacturing through non-destructive analysis using hyperspectral imaging. Results show that shot-wave infrared (SWIR) data is more effective for analyzing peat samples and predicting total phenol levels, with accuracies up to 99.81%. | [
"['Yijun Yan' 'Jinchang Ren' 'Barry Harrison' 'Oliver Lewis' 'Yinhe Li'\n 'Ping Ma']"
]
|
null | null | 2405.02195 | null | null | http://arxiv.org/pdf/2405.02195v1 | 2024-05-03T15:51:02Z | 2024-05-03T15:51:02Z | Impact of emoji exclusion on the performance of Arabic sarcasm detection
models | The complex challenge of detecting sarcasm in Arabic speech on social media is increased by the language diversity and the nature of sarcastic expressions. There is a significant gap in the capability of existing models to effectively interpret sarcasm in Arabic, which mandates the necessity for more sophisticated and precise detection methods. In this paper, we investigate the impact of a fundamental preprocessing component on sarcasm speech detection. While emojis play a crucial role in mitigating the absence effect of body language and facial expressions in modern communication, their impact on automated text analysis, particularly in sarcasm detection, remains underexplored. We investigate the impact of emoji exclusion from datasets on the performance of sarcasm detection models in social media content for Arabic as a vocabulary-super rich language. This investigation includes the adaptation and enhancement of AraBERT pre-training models, specifically by excluding emojis, to improve sarcasm detection capabilities. We use AraBERT pre-training to refine the specified models, demonstrating that the removal of emojis can significantly boost the accuracy of sarcasm detection. This approach facilitates a more refined interpretation of language, eliminating the potential confusion introduced by non-textual elements. The evaluated AraBERT models, through the focused strategy of emoji removal, adeptly navigate the complexities of Arabic sarcasm. This study establishes new benchmarks in Arabic natural language processing and presents valuable insights for social media platforms. | [
"['Ghalyah H. Aleryani' 'Wael Deabes' 'Khaled Albishre'\n 'Alaa E. Abdel-Hakim']"
]
|
null | null | 2405.02200 | null | null | http://arxiv.org/pdf/2405.02200v2 | 2024-05-25T13:14:36Z | 2024-05-03T15:57:22Z | Position: Why We Must Rethink Empirical Research in Machine Learning | We warn against a common but incomplete understanding of empirical research in machine learning that leads to non-replicable results, makes findings unreliable, and threatens to undermine progress in the field. To overcome this alarming situation, we call for more awareness of the plurality of ways of gaining knowledge experimentally but also of some epistemic limitations. In particular, we argue most current empirical machine learning research is fashioned as confirmatory research while it should rather be considered exploratory. | [
"['Moritz Herrmann' 'F. Julian D. Lange' 'Katharina Eggensperger'\n 'Giuseppe Casalicchio' 'Marcel Wever' 'Matthias Feurer' 'David Rügamer'\n 'Eyke Hüllermeier' 'Anne-Laure Boulesteix' 'Bernd Bischl']"
]
|
null | null | 2405.02201 | null | null | http://arxiv.org/pdf/2405.02201v2 | 2024-05-29T11:12:24Z | 2024-05-03T15:57:26Z | Regularized Q-learning through Robust Averaging | We propose a new Q-learning variant, called 2RA Q-learning, that addresses some weaknesses of existing Q-learning methods in a principled manner. One such weakness is an underlying estimation bias which cannot be controlled and often results in poor performance. We propose a distributionally robust estimator for the maximum expected value term, which allows us to precisely control the level of estimation bias introduced. The distributionally robust estimator admits a closed-form solution such that the proposed algorithm has a computational cost per iteration comparable to Watkins' Q-learning. For the tabular case, we show that 2RA Q-learning converges to the optimal policy and analyze its asymptotic mean-squared error. Lastly, we conduct numerical experiments for various settings, which corroborate our theoretical findings and indicate that 2RA Q-learning often performs better than existing methods. | [
"['Peter Schmitt-Förster' 'Tobias Sutter']"
]
|
null | null | 2405.02213 | null | null | http://arxiv.org/pdf/2405.02213v2 | 2024-05-15T16:33:57Z | 2024-05-03T16:19:24Z | Automatic Programming: Large Language Models and Beyond | Automatic programming has seen increasing popularity due to the emergence of tools like GitHub Copilot which rely on Large Language Models (LLMs). At the same time, automatically generated code faces challenges during deployment due to concerns around quality and trust. In this article, we study automated coding in a general sense and study the concerns around code quality, security and related issues of programmer responsibility. These are key issues for organizations while deciding on the usage of automatically generated code. We discuss how advances in software engineering such as program repair and analysis can enable automatic programming. We conclude with a forward looking view, focusing on the programming environment of the near future, where programmers may need to switch to different roles to fully utilize the power of automatic programming. Automated repair of automatically generated programs from LLMs, can help produce higher assurance code from LLMs, along with evidence of assurance | [
"['Michael R. Lyu' 'Baishakhi Ray' 'Abhik Roychoudhury' 'Shin Hwei Tan'\n 'Patanamon Thongtanunam']"
]
|
null | null | 2405.02220 | null | null | http://arxiv.org/pdf/2405.02220v2 | 2024-05-09T16:02:12Z | 2024-05-03T16:27:39Z | Designed Dithering Sign Activation for Binary Neural Networks | Binary Neural Networks emerged as a cost-effective and energy-efficient solution for computer vision tasks by binarizing either network weights or activations. However, common binary activations, such as the Sign activation function, abruptly binarize the values with a single threshold, losing fine-grained details in the feature outputs. This work proposes an activation that applies multiple thresholds following dithering principles, shifting the Sign activation function for each pixel according to a spatially periodic threshold kernel. Unlike literature methods, the shifting is defined jointly for a set of adjacent pixels, taking advantage of spatial correlations. Experiments over the classification task demonstrate the effectiveness of the designed dithering Sign activation function as an alternative activation for binary neural networks, without increasing the computational cost. Further, DeSign balances the preservation of details with the efficiency of binary operations. | [
"['Brayan Monroy' 'Juan Estupiñan' 'Tatiana Gelvez-Barrera' 'Jorge Bacca'\n 'Henry Arguello']"
]
|
null | null | 2405.02221 | null | null | http://arxiv.org/pdf/2405.02221v1 | 2024-05-03T16:28:05Z | 2024-05-03T16:28:05Z | Discretization Error of Fourier Neural Operators | Operator learning is a variant of machine learning that is designed to approximate maps between function spaces from data. The Fourier Neural Operator (FNO) is a common model architecture used for operator learning. The FNO combines pointwise linear and nonlinear operations in physical space with pointwise linear operations in Fourier space, leading to a parameterized map acting between function spaces. Although FNOs formally involve convolutions of functions on a continuum, in practice the computations are performed on a discretized grid, allowing efficient implementation via the FFT. In this paper, the aliasing error that results from such a discretization is quantified and algebraic rates of convergence in terms of the grid resolution are obtained as a function of the regularity of the input. Numerical experiments that validate the theory and describe model stability are performed. | [
"['Samuel Lanthaler' 'Andrew M. Stuart' 'Margaret Trautner']"
]
|
null | null | 2405.02225 | null | null | http://arxiv.org/pdf/2405.02225v1 | 2024-05-03T16:32:09Z | 2024-05-03T16:32:09Z | Fair Risk Control: A Generalized Framework for Calibrating Multi-group
Fairness Risks | This paper introduces a framework for post-processing machine learning models so that their predictions satisfy multi-group fairness guarantees. Based on the celebrated notion of multicalibration, we introduce $(mathbf{s},mathcal{G}, alpha)-$GMC (Generalized Multi-Dimensional Multicalibration) for multi-dimensional mappings $mathbf{s}$, constraint set $mathcal{G}$, and a pre-specified threshold level $alpha$. We propose associated algorithms to achieve this notion in general settings. This framework is then applied to diverse scenarios encompassing different fairness concerns, including false negative rate control in image segmentation, prediction set conditional uncertainty quantification in hierarchical classification, and de-biased text generation in language models. We conduct numerical studies on several datasets and tasks. | [
"['Lujing Zhang' 'Aaron Roth' 'Linjun Zhang']"
]
|
null | null | 2405.02235 | null | null | http://arxiv.org/pdf/2405.02235v2 | 2024-05-30T15:18:24Z | 2024-05-03T16:45:15Z | Learning Optimal Deterministic Policies with Stochastic Policy Gradients | Policy gradient (PG) methods are successful approaches to deal with continuous reinforcement learning (RL) problems. They learn stochastic parametric (hyper)policies by either exploring in the space of actions or in the space of parameters. Stochastic controllers, however, are often undesirable from a practical perspective because of their lack of robustness, safety, and traceability. In common practice, stochastic (hyper)policies are learned only to deploy their deterministic version. In this paper, we make a step towards the theoretical understanding of this practice. After introducing a novel framework for modeling this scenario, we study the global convergence to the best deterministic policy, under (weak) gradient domination assumptions. Then, we illustrate how to tune the exploration level used for learning to optimize the trade-off between the sample complexity and the performance of the deployed deterministic policy. Finally, we quantitatively compare action-based and parameter-based exploration, giving a formal guise to intuitive results. | [
"['Alessandro Montenegro' 'Marco Mussi' 'Alberto Maria Metelli'\n 'Matteo Papini']"
]
|
null | null | 2405.02240 | null | null | http://arxiv.org/pdf/2405.02240v1 | 2024-05-03T16:51:18Z | 2024-05-03T16:51:18Z | Subgraph2vec: A random walk-based algorithm for embedding knowledge
graphs | Graph is an important data representation which occurs naturally in the real world applications cite{goyal2018graph}. Therefore, analyzing graphs provides users with better insights in different areas such as anomaly detection cite{ma2021comprehensive}, decision making cite{fan2023graph}, clustering cite{tsitsulin2023graph}, classification cite{wang2021mixup} and etc. However, most of these methods require high levels of computational time and space. We can use other ways like embedding to reduce these costs. Knowledge graph (KG) embedding is a technique that aims to achieve the vector representation of a KG. It represents entities and relations of a KG in a low-dimensional space while maintaining the semantic meanings of them. There are different methods for embedding graphs including random walk-based methods such as node2vec, metapath2vec and regpattern2vec. However, most of these methods bias the walks based on a rigid pattern usually hard-coded in the algorithm. In this work, we introduce textit{subgraph2vec} for embedding KGs where walks are run inside a user-defined subgraph. We use this embedding for link prediction and prove our method has better performance in most cases in comparison with the previous ones. | [
"['Elika Bozorgi' 'Saber Soleimani' 'Sakher Khalil Alqaiidi'\n 'Hamid Reza Arabnia' 'Krzysztof Kochut']"
]
|
null | null | 2405.02267 | null | null | http://arxiv.org/pdf/2405.02267v1 | 2024-05-03T17:34:57Z | 2024-05-03T17:34:57Z | Structural Pruning of Pre-trained Language Models via Neural
Architecture Search | Pre-trained language models (PLM), for example BERT or RoBERTa, mark the state-of-the-art for natural language understanding task when fine-tuned on labeled data. However, their large size poses challenges in deploying them for inference in real-world applications, due to significant GPU memory requirements and high inference latency. This paper explores neural architecture search (NAS) for structural pruning to find sub-parts of the fine-tuned network that optimally trade-off efficiency, for example in terms of model size or latency, and generalization performance. We also show how we can utilize more recently developed two-stage weight-sharing NAS approaches in this setting to accelerate the search process. Unlike traditional pruning methods with fixed thresholds, we propose to adopt a multi-objective approach that identifies the Pareto optimal set of sub-networks, allowing for a more flexible and automated compression process. | [
"['Aaron Klein' 'Jacek Golebiowski' 'Xingchen Ma' 'Valerio Perrone'\n 'Cedric Archambeau']"
]
|
null | null | 2405.02292 | null | null | http://arxiv.org/pdf/2405.02292v1 | 2024-02-07T23:58:10Z | 2024-02-07T23:58:10Z | ALOHA 2: An Enhanced Low-Cost Hardware for Bimanual Teleoperation | Diverse demonstration datasets have powered significant advances in robot learning, but the dexterity and scale of such data can be limited by the hardware cost, the hardware robustness, and the ease of teleoperation. We introduce ALOHA 2, an enhanced version of ALOHA that has greater performance, ergonomics, and robustness compared to the original design. To accelerate research in large-scale bimanual manipulation, we open source all hardware designs of ALOHA 2 with a detailed tutorial, together with a MuJoCo model of ALOHA 2 with system identification. See the project website at aloha-2.github.io. | [
"['ALOHA 2 Team' 'Jorge Aldaco' 'Travis Armstrong' 'Robert Baruch'\n 'Jeff Bingham' 'Sanky Chan' 'Kenneth Draper' 'Debidatta Dwibedi'\n 'Chelsea Finn' 'Pete Florence' 'Spencer Goodrich' 'Wayne Gramlich'\n 'Torr Hage' 'Alexander Herzog' 'Jonathan Hoech' 'Thinh Nguyen'\n 'Ian Storz' 'Baruch Tabanpour' 'Leila Takayama' 'Jonathan Tompson'\n 'Ayzaan Wahid' 'Ted Wahrburg' 'Sichun Xu' 'Sergey Yaroshenko'\n 'Kevin Zakka' 'Tony Z. Zhao']"
]
|
null | null | 2405.02295 | null | null | http://arxiv.org/pdf/2405.02295v1 | 2024-03-06T16:46:07Z | 2024-03-06T16:46:07Z | Neural Additive Image Model: Interpretation through Interpolation | Understanding how images influence the world, interpreting which effects their semantics have on various quantities and exploring the reasons behind changes in image-based predictions are highly difficult yet extremely interesting problems. By adopting a holistic modeling approach utilizing Neural Additive Models in combination with Diffusion Autoencoders, we can effectively identify the latent hidden semantics of image effects and achieve full intelligibility of additional tabular effects. Our approach offers a high degree of flexibility, empowering us to comprehensively explore the impact of various image characteristics. We demonstrate that the proposed method can precisely identify complex image effects in an ablation study. To further showcase the practical applicability of our proposed model, we conduct a case study in which we investigate how the distinctive features and attributes captured within host images exert influence on the pricing of Airbnb rentals. | [
"['Arik Reuter' 'Anton Thielmann' 'Benjamin Saefken']"
]
|
null | null | 2405.02299 | null | null | http://arxiv.org/pdf/2405.02299v2 | 2024-05-07T02:00:58Z | 2024-03-11T12:33:33Z | Deep Reinforcement Learning for Modelling Protein Complexes | AlphaFold can be used for both single-chain and multi-chain protein structure prediction, while the latter becomes extremely challenging as the number of chains increases. In this work, by taking each chain as a node and assembly actions as edges, we show that an acyclic undirected connected graph can be used to predict the structure of multi-chain protein complexes (a.k.a., protein complex modelling, PCM). However, there are still two challenges: 1) The huge combinatorial optimization space of $N^{N-2}$ ($N$ is the number of chains) for the PCM problem can easily lead to high computational cost. 2) The scales of protein complexes exhibit distribution shift due to variance in chain numbers, which calls for the generalization in modelling complexes of various scales. To address these challenges, we propose GAPN, a Generative Adversarial Policy Network powered by domain-specific rewards and adversarial loss through policy gradient for automatic PCM prediction. Specifically, GAPN learns to efficiently search through the immense assembly space and optimize the direct docking reward through policy gradient. Importantly, we design an adversarial reward function to enhance the receptive field of our model. In this way, GAPN will simultaneously focus on a specific batch of complexes and the global assembly rules learned from complexes with varied chain numbers. Empirically, we have achieved both significant accuracy (measured by RMSD and TM-Score) and efficiency improvements compared to leading PCM softwares. | [
"['Ziqi Gao' 'Tao Feng' 'Jiaxuan You' 'Chenyi Zi' 'Yan Zhou' 'Chen Zhang'\n 'Jia Li']"
]
|
null | null | 2405.02316 | null | null | http://arxiv.org/pdf/2405.02316v1 | 2024-04-12T22:34:17Z | 2024-04-12T22:34:17Z | A Cloud-Edge Framework for Energy-Efficient Event-Driven Control: An
Integration of Online Supervised Learning, Spiking Neural Networks and Local
Plasticity Rules | This paper presents a novel cloud-edge framework for addressing computational and energy constraints in complex control systems. Our approach centers around a learning-based controller using Spiking Neural Networks (SNN) on physical plants. By integrating a biologically plausible learning method with local plasticity rules, we harness the efficiency, scalability, and low latency of SNNs. This design replicates control signals from a cloud-based controller directly on the plant, reducing the need for constant plant-cloud communication. The plant updates weights only when errors surpass predefined thresholds, ensuring efficiency and robustness in various conditions. Applied to linear workbench systems and satellite rendezvous scenarios, including obstacle avoidance, our architecture dramatically lowers normalized tracking error by 96% with increased network size. The event-driven nature of SNNs minimizes energy consumption, utilizing only about 111 nJ (0.3% of conventional computing requirements). The results demonstrate the system's adjustment to changing work environments and its efficient use of computational and energy resources, with a moderate increase in energy consumption of 27.2% and 37% for static and dynamic obstacles, respectively, compared to non-obstacle scenarios. | [
"['Reza Ahmadvand' 'Sarah Safura Sharif' 'Yaser Mike Banad']"
]
|
null | null | 2405.02318 | null | null | http://arxiv.org/pdf/2405.02318v1 | 2024-04-18T00:20:48Z | 2024-04-18T00:20:48Z | NL2FOL: Translating Natural Language to First-Order Logic for Logical
Fallacy Detection | Logical fallacies are common errors in reasoning that undermine the logic of an argument. Automatically detecting logical fallacies has important applications in tracking misinformation and validating claims. In this paper, we design a process to reliably detect logical fallacies by translating natural language to First-order Logic (FOL) step-by-step using Large Language Models (LLMs). We then utilize Satisfiability Modulo Theory (SMT) solvers to reason about the validity of the formula and classify inputs as either a fallacy or valid statement. Our model also provides a novel means of utilizing LLMs to interpret the output of the SMT solver, offering insights into the counter-examples that illustrate why a given sentence is considered a logical fallacy. Our approach is robust, interpretable and does not require training data or fine-tuning. We evaluate our model on a mixed dataset of fallacies and valid sentences. The results demonstrate improved performance compared to end-to-end LLMs, with our classifier achieving an F1-score of 71% on the Logic dataset. The approach is able to generalize effectively, achieving an F1-score of 73% on the challenge set, LogicClimate, outperforming state-of-the-art models by 21% despite its much smaller size. | [
"['Abhinav Lalwani' 'Lovish Chopra' 'Christopher Hahn' 'Caroline Trippel'\n 'Zhijing Jin' 'Mrinmaya Sachan']"
]
|
null | null | 2405.02323 | null | null | http://arxiv.org/pdf/2405.02323v1 | 2024-04-22T09:13:47Z | 2024-04-22T09:13:47Z | CNN-Based Equalization for Communications: Achieving Gigabit Throughput
with a Flexible FPGA Hardware Architecture | To satisfy the growing throughput demand of data-intensive applications, the performance of optical communication systems increased dramatically in recent years. With higher throughput, more advanced equalizers are crucial, to compensate for impairments caused by inter-symbol interference (ISI). The latest research shows that artificial neural network (ANN)-based equalizers are promising candidates to replace traditional algorithms for high-throughput communications. On the other hand, not only throughput but also flexibility is a main objective of beyond-5G and 6G communication systems. A platform that is able to satisfy the strict throughput and flexibility requirements of modern communication systems are field programmable gate arrays (FPGAs). Thus, in this work, we present a high-performance FPGA implementation of an ANN-based equalizer, which meets the throughput requirements of modern optical communication systems. Further, our architecture is highly flexible since it includes a variable degree of parallelism (DOP) and therefore can also be applied to low-cost or low-power applications which is demonstrated for a magnetic recording channel. The implementation is based on a cross-layer design approach featuring optimizations from the algorithm down to the hardware architecture, including a detailed quantization analysis. Moreover, we present a framework to reduce the latency of the ANN-based equalizer under given throughput constraints. As a result, the bit error ratio (BER) of our equalizer for the optical fiber channel is around four times lower than that of a conventional one, while the corresponding FPGA implementation achieves a throughput of more than 40 GBd, outperforming a high-performance graphics processing unit (GPU) by three orders of magnitude for a similar batch size. | [
"['Jonas Ney' 'Christoph Füllner' 'Vincent Lauinger' 'Laurent Schmalen'\n 'Sebastian Randel' 'Norbert Wehn']"
]
|
null | null | 2405.02326 | null | null | http://arxiv.org/pdf/2405.02326v1 | 2024-04-23T18:55:49Z | 2024-04-23T18:55:49Z | Evaluating LLMs for Hardware Design and Test | Large Language Models (LLMs) have demonstrated capabilities for producing code in Hardware Description Languages (HDLs). However, most of the focus remains on their abilities to write functional code, not test code. The hardware design process consists of both design and test, and so eschewing validation and verification leaves considerable potential benefit unexplored, given that a design and test framework may allow for progress towards full automation of the digital design pipeline. In this work, we perform one of the first studies exploring how a LLM can both design and test hardware modules from provided specifications. Using a suite of 8 representative benchmarks, we examined the capabilities and limitations of the state-of-the-art conversational LLMs when producing Verilog for functional and verification purposes. We taped out the benchmarks on a Skywater 130nm shuttle and received the functional chip. | [
"['Jason Blocklove' 'Siddharth Garg' 'Ramesh Karri' 'Hammond Pearce']"
]
|
null | null | 2405.02330 | null | null | http://arxiv.org/pdf/2405.02330v1 | 2024-04-25T13:49:50Z | 2024-04-25T13:49:50Z | Adaptive Semantic Token Selection for AI-native Goal-oriented
Communications | In this paper, we propose a novel design for AI-native goal-oriented communications, exploiting transformer neural networks under dynamic inference constraints on bandwidth and computation. Transformers have become the standard architecture for pretraining large-scale vision and text models, and preliminary results have shown promising performance also in deep joint source-channel coding (JSCC). Here, we consider a dynamic model where communication happens over a channel with variable latency and bandwidth constraints. Leveraging recent works on conditional computation, we exploit the structure of the transformer blocks and the multihead attention operator to design a trainable semantic token selection mechanism that learns to select relevant tokens (e.g., image patches) from the input signal. This is done dynamically, on a per-input basis, with a rate that can be chosen as an additional input by the user. We show that our model improves over state-of-the-art token selection mechanisms, exhibiting high accuracy for a wide range of latency and bandwidth constraints, without the need for deploying multiple architectures tailored to each constraint. Last, but not least, the proposed token selection mechanism helps extract powerful semantics that are easy to understand and explain, paving the way for interpretable-by-design models for the next generation of AI-native communication systems. | [
"['Alessio Devoto' 'Simone Petruzzi' 'Jary Pomponi' 'Paolo Di Lorenzo'\n 'Simone Scardapane']"
]
|
null | null | 2405.02334 | null | null | http://arxiv.org/pdf/2405.02334v1 | 2024-04-26T15:02:39Z | 2024-04-26T15:02:39Z | Rad4XCNN: a new agnostic method for post-hoc global explanation of
CNN-derived features by means of radiomics | In the last years, artificial intelligence (AI) in clinical decision support systems (CDSS) played a key role in harnessing machine learning and deep learning architectures. Despite their promising capabilities, the lack of transparency and explainability of AI models poses significant challenges, particularly in medical contexts where reliability is a mandatory aspect. Achieving transparency without compromising predictive accuracy remains a key challenge. This paper presents a novel method, namely Rad4XCNN, to enhance the predictive power of CNN-derived features with the interpretability inherent in radiomic features. Rad4XCNN diverges from conventional methods based on saliency map, by associating intelligible meaning to CNN-derived features by means of Radiomics, offering new perspectives on explanation methods beyond visualization maps. Using a breast cancer classification task as a case study, we evaluated Rad4XCNN on ultrasound imaging datasets, including an online dataset and two in-house datasets for internal and external validation. Some key results are: i) CNN-derived features guarantee more robust accuracy when compared against ViT-derived and radiomic features; ii) conventional visualization map methods for explanation present several pitfalls; iii) Rad4XCNN does not sacrifice model accuracy for their explainability; iv) Rad4XCNN provides global explanation insights enabling the physician to analyze the model outputs and findings. In addition, we highlight the importance of integrating interpretability into AI models for enhanced trust and adoption in clinical practice, emphasizing how our method can mitigate some concerns related to explainable AI methods. | [
"['Francesco Prinzi' 'Carmelo Militello' 'Calogero Zarcaro'\n 'Tommaso Vincenzo Bartolotta' 'Salvatore Gaglio' 'Salvatore Vitabile']"
]
|
null | null | 2405.02335 | null | null | http://arxiv.org/pdf/2405.02335v1 | 2024-04-26T19:34:03Z | 2024-04-26T19:34:03Z | sDAC -- Semantic Digital Analog Converter for Semantic Communications | In this paper, we propose a novel semantic digital analog converter (sDAC) for the compatibility of semantic communications and digital communications. Most of the current semantic communication systems are based on the analog modulations, ignoring their incorporation with digital communication systems, which are more common in practice. In fact, quantization methods in traditional communication systems are not appropriate for use in the era of semantic communication as these methods do not consider the semantic information inside symbols. In this case, any bit flip caused by channel noise can lead to a great performance drop. To address this challenge, sDAC is proposed. It is a simple yet efficient and generative module used to realize digital and analog bi-directional conversion. On the transmitter side, continuous values from the encoder are converted to binary bits and then can be modulated by any existing methods. After transmitting through the noisy channel, these bits get demodulated by paired methods and converted back to continuous values for further semantic decoding. The whole progress does not depend on any specific semantic model, modulation methods, or channel conditions. In the experiment section, the performance of sDAC is tested across different semantic models, semantic tasks, modulation methods, channel conditions and quantization orders. Test results show that the proposed sDAC has great generative properties and channel robustness. | [
"['Zhicheng Bao' 'Chen Dong' 'Xiaodong Xu']"
]
|
null | null | 2405.02336 | null | null | http://arxiv.org/pdf/2405.02336v1 | 2024-04-29T04:51:05Z | 2024-04-29T04:51:05Z | Artificial General Intelligence (AGI)-Native Wireless Systems: A Journey
Beyond 6G | Building future wireless systems that support services like digital twins (DTs) is challenging to achieve through advances to conventional technologies like meta-surfaces. While artificial intelligence (AI)-native networks promise to overcome some limitations of wireless technologies, developments still rely on AI tools like neural networks. Such tools struggle to cope with the non-trivial challenges of the network environment and the growing demands of emerging use cases. In this paper, we revisit the concept of AI-native wireless systems, equipping them with the common sense necessary to transform them into artificial general intelligence (AGI)-native systems. These systems acquire common sense by exploiting different cognitive abilities such as perception, analogy, and reasoning, that enable them to generalize and deal with unforeseen scenarios. Towards developing the components of such a system, we start by showing how the perception module can be built through abstracting real-world elements into generalizable representations. These representations are then used to create a world model, founded on principles of causality and hyper-dimensional (HD) computing, that aligns with intuitive physics and enables analogical reasoning, that define common sense. Then, we explain how methods such as integrated information theory play a role in the proposed intent-driven and objective-driven planning methods that maneuver the AGI-native network to take actions. Next, we discuss how an AGI-native network can enable use cases related to human and autonomous agents: a) analogical reasoning for next-generation DTs, b) synchronized and resilient experiences for cognitive avatars, and c) brain-level metaverse experiences like holographic teleportation. Finally, we conclude with a set of recommendations to build AGI-native systems. Ultimately, we envision this paper as a roadmap for the beyond 6G era. | [
"['Walid Saad' 'Omar Hashash' 'Christo Kurisummoottil Thomas'\n 'Christina Chaccour' 'Merouane Debbah' 'Narayan Mandayam' 'Zhu Han']"
]
|
null | null | 2405.02340 | null | null | http://arxiv.org/pdf/2405.02340v1 | 2024-05-01T21:00:02Z | 2024-05-01T21:00:02Z | A Comprehensive Approach to Carbon Dioxide Emission Analysis in High
Human Development Index Countries using Statistical and Machine Learning
Techniques | Reducing Carbon dioxide (CO2) emission is vital at both global and national levels, given their significant role in exacerbating climate change. CO2 emission, stemming from a variety of industrial and economic activities, are major contributors to the greenhouse effect and global warming, posing substantial obstacles in addressing climate issues. It's imperative to forecast CO2 emission trends and classify countries based on their emission patterns to effectively mitigate worldwide carbon emission. This paper presents an in-depth comparative study on the determinants of CO2 emission in twenty countries with high Human Development Index (HDI), exploring factors related to economy, environment, energy use, and renewable resources over a span of 25 years. The study unfolds in two distinct phases: initially, statistical techniques such as Ordinary Least Squares (OLS), fixed effects, and random effects models are applied to pinpoint significant determinants of CO2 emission. Following this, the study leverages supervised and unsupervised machine learning (ML) methods to further scrutinize and understand the factors influencing CO2 emission. Seasonal AutoRegressive Integrated Moving Average with eXogenous variables (SARIMAX), a supervised ML model, is first used to predict emission trends from historical data, offering practical insights for policy formulation. Subsequently, Dynamic Time Warping (DTW), an unsupervised learning approach, is used to group countries by similar emission patterns. The dual-phase approach utilized in this study significantly improves the accuracy of CO2 emission predictions while also providing a deeper insight into global emission trends. By adopting this thorough analytical framework, nations can develop more focused and effective carbon reduction policies, playing a vital role in the global initiative to combat climate change. | [
"['Hamed Khosravi' 'Ahmed Shoyeb Raihan' 'Farzana Islam' 'Ashish Nimbarte'\n 'Imtiaz Ahmed']"
]
|
null | null | 2405.02341 | null | null | http://arxiv.org/pdf/2405.02341v1 | 2024-05-02T03:48:47Z | 2024-05-02T03:48:47Z | Improved Communication-Privacy Trade-offs in $L_2$ Mean Estimation under
Streaming Differential Privacy | We study $L_2$ mean estimation under central differential privacy and communication constraints, and address two key challenges: firstly, existing mean estimation schemes that simultaneously handle both constraints are usually optimized for $L_infty$ geometry and rely on random rotation or Kashin's representation to adapt to $L_2$ geometry, resulting in suboptimal leading constants in mean square errors (MSEs); secondly, schemes achieving order-optimal communication-privacy trade-offs do not extend seamlessly to streaming differential privacy (DP) settings (e.g., tree aggregation or matrix factorization), rendering them incompatible with DP-FTRL type optimizers. In this work, we tackle these issues by introducing a novel privacy accounting method for the sparsified Gaussian mechanism that incorporates the randomness inherent in sparsification into the DP noise. Unlike previous approaches, our accounting algorithm directly operates in $L_2$ geometry, yielding MSEs that fast converge to those of the uncompressed Gaussian mechanism. Additionally, we extend the sparsification scheme to the matrix factorization framework under streaming DP and provide a precise accountant tailored for DP-FTRL type optimizers. Empirically, our method demonstrates at least a 100x improvement of compression for DP-SGD across various FL tasks. | [
"['Wei-Ning Chen' 'Berivan Isik' 'Peter Kairouz' 'Albert No' 'Sewoong Oh'\n 'Zheng Xu']"
]
|
null | null | 2405.02344 | null | null | http://arxiv.org/pdf/2405.02344v1 | 2024-05-02T13:48:37Z | 2024-05-02T13:48:37Z | Backdoor-based Explainable AI Benchmark for High Fidelity Evaluation of
Attribution Methods | Attribution methods compute importance scores for input features to explain the output predictions of deep models. However, accurate assessment of attribution methods is challenged by the lack of benchmark fidelity for attributing model predictions. Moreover, other confounding factors in attribution estimation, including the setup choices of post-processing techniques and explained model predictions, further compromise the reliability of the evaluation. In this work, we first identify a set of fidelity criteria that reliable benchmarks for attribution methods are expected to fulfill, thereby facilitating a systematic assessment of attribution benchmarks. Next, we introduce a Backdoor-based eXplainable AI benchmark (BackX) that adheres to the desired fidelity criteria. We theoretically establish the superiority of our approach over the existing benchmarks for well-founded attribution evaluation. With extensive analysis, we also identify a setup for a consistent and fair benchmarking of attribution methods across different underlying methodologies. This setup is ultimately employed for a comprehensive comparison of existing methods using our BackX benchmark. Finally, our analysis also provides guidance for defending against backdoor attacks with the help of attribution methods. | [
"['Peiyu Yang' 'Naveed Akhtar' 'Jiantong Jiang' 'Ajmal Mian']"
]
|
null | null | 2405.02346 | null | null | http://arxiv.org/pdf/2405.02346v1 | 2024-05-02T15:15:05Z | 2024-05-02T15:15:05Z | Temporal assessment of malicious behaviors: application to turnout field
data monitoring | Monitored data collected from railway turnouts are vulnerable to cyberattacks: attackers may either conceal failures or trigger unnecessary maintenance actions. To address this issue, a cyberattack investigation method is proposed based on predictions made from the temporal evolution of the turnout behavior. These predictions are then compared to the field acquired data to detect any discrepancy. This method is illustrated on a collection of real-life data. | [
"['Sara Abdellaoui' 'Emil Dumitrescu' 'Cédric Escudero' 'Eric Zamaï']"
]
|
null | null | 2405.02347 | null | null | http://arxiv.org/pdf/2405.02347v2 | 2024-06-14T18:06:47Z | 2024-05-02T18:24:41Z | COPAL: Continual Pruning in Large Language Generative Models | Adapting pre-trained large language models to different domains in natural language processing requires two key considerations: high computational demands and model's inability to continual adaptation. To simultaneously address both issues, this paper presents COPAL (COntinual Pruning in Adaptive Language settings), an algorithm developed for pruning large language generative models under a continual model adaptation setting. While avoiding resource-heavy finetuning or retraining, our pruning process is guided by the proposed sensitivity analysis. The sensitivity effectively measures model's ability to withstand perturbations introduced by the new dataset and finds model's weights that are relevant for all encountered datasets. As a result, COPAL allows seamless model adaptation to new domains while enhancing the resource efficiency. Our empirical evaluation on a various size of LLMs show that COPAL outperforms baseline models, demonstrating its efficacy in efficiency and adaptability. | [
"['Srikanth Malla' 'Joon Hee Choi' 'Chiho Choi']"
]
|
null | null | 2405.02349 | null | null | http://arxiv.org/pdf/2405.02349v2 | 2024-05-07T13:21:55Z | 2024-05-02T19:06:20Z | Explainable Multi-Label Classification of MBTI Types | In this study, we aim to identify the most effective machine learning model for accurately classifying Myers-Briggs Type Indicator (MBTI) types from Reddit posts and a Kaggle data set. We apply multi-label classification using the Binary Relevance method. We use Explainable Artificial Intelligence (XAI) approach to highlight the transparency and understandability of the process and result. To achieve this, we experiment with glass-box learning models, i.e. models designed for simplicity, transparency, and interpretability. We selected k-Nearest Neighbour, Multinomial Naive Bayes, and Logistic Regression for the glass-box models. We show that Multinomial Naive Bayes and k-Nearest Neighbour perform better if classes with Observer (S) traits are excluded, whereas Logistic Regression obtains its best results when all classes have > 550 entries. | [
"['Siana Kong' 'Marina Sokolova']"
]
|
null | null | 2405.02350 | null | null | http://arxiv.org/pdf/2405.02350v1 | 2024-05-02T20:10:27Z | 2024-05-02T20:10:27Z | What makes Models Compositional? A Theoretical View: With Supplement | Compositionality is thought to be a key component of language, and various compositional benchmarks have been developed to empirically probe the compositional generalization of existing sequence processing models. These benchmarks often highlight failures of existing models, but it is not clear why these models fail in this way. In this paper, we seek to theoretically understand the role the compositional structure of the models plays in these failures and how this structure relates to their expressivity and sample complexity. We propose a general neuro-symbolic definition of compositional functions and their compositional complexity. We then show how various existing general and special purpose sequence processing models (such as recurrent, convolution and attention-based ones) fit this definition and use it to analyze their compositional complexity. Finally, we provide theoretical guarantees for the expressivity and systematic generalization of compositional models that explicitly depend on our proposed definition and highlighting factors which drive poor empirical performance. | [
"['Parikshit Ram' 'Tim Klinger' 'Alexander G. Gray']"
]
|
null | null | 2405.02351 | null | null | http://arxiv.org/pdf/2405.02351v2 | 2024-06-14T23:20:23Z | 2024-05-02T21:08:49Z | Towards General Neural Surrogate Solvers with Specialized Neural
Accelerators | Surrogate neural network-based partial differential equation (PDE) solvers have the potential to solve PDEs in an accelerated manner, but they are largely limited to systems featuring fixed domain sizes, geometric layouts, and boundary conditions. We propose Specialized Neural Accelerator-Powered Domain Decomposition Methods (SNAP-DDM), a DDM-based approach to PDE solving in which subdomain problems containing arbitrary boundary conditions and geometric parameters are accurately solved using an ensemble of specialized neural operators. We tailor SNAP-DDM to 2D electromagnetics and fluidic flow problems and show how innovations in network architecture and loss function engineering can produce specialized surrogate subdomain solvers with near unity accuracy. We utilize these solvers with standard DDM algorithms to accurately solve freeform electromagnetics and fluids problems featuring a wide range of domain sizes. | [
"['Chenkai Mao' 'Robert Lupoiu' 'Tianxiang Dai' 'Mingkun Chen'\n 'Jonathan A. Fan']"
]
|
null | null | 2405.02353 | null | null | http://arxiv.org/pdf/2405.02353v1 | 2024-05-02T23:03:45Z | 2024-05-02T23:03:45Z | Early Transformers: A study on Efficient Training of Transformer Models
through Early-Bird Lottery Tickets | The training of Transformer models has revolutionized natural language processing and computer vision, but it remains a resource-intensive and time-consuming process. This paper investigates the applicability of the early-bird ticket hypothesis to optimize the training efficiency of Transformer models. We propose a methodology that combines iterative pruning, masked distance calculation, and selective retraining to identify early-bird tickets in various Transformer architectures, including ViT, Swin-T, GPT-2, and RoBERTa. Our experimental results demonstrate that early-bird tickets can be consistently found within the first few epochs of training or fine-tuning, enabling significant resource optimization without compromising performance. The pruned models obtained from early-bird tickets achieve comparable or even superior accuracy to their unpruned counterparts while substantially reducing memory usage. Furthermore, our comparative analysis highlights the generalizability of the early-bird ticket phenomenon across different Transformer models and tasks. This research contributes to the development of efficient training strategies for Transformer models, making them more accessible and resource-friendly. By leveraging early-bird tickets, practitioners can accelerate the progress of natural language processing and computer vision applications while reducing the computational burden associated with training Transformer models. | [
"['Shravan Cheekati']"
]
|
null | null | 2405.02354 | null | null | http://arxiv.org/pdf/2405.02354v1 | 2024-05-03T02:15:05Z | 2024-05-03T02:15:05Z | Heterogeneous network and graph attention auto-encoder for
LncRNA-disease association prediction | The emerging research shows that lncRNAs are associated with a series of complex human diseases. However, most of the existing methods have limitations in identifying nonlinear lncRNA-disease associations (LDAs), and it remains a huge challenge to predict new LDAs. Therefore, the accurate identification of LDAs is very important for the warning and treatment of diseases. In this work, multiple sources of biomedical data are fully utilized to construct characteristics of lncRNAs and diseases, and linear and nonlinear characteristics are effectively integrated. Furthermore, a novel deep learning model based on graph attention automatic encoder is proposed, called HGATELDA. To begin with, the linear characteristics of lncRNAs and diseases are created by the miRNA-lncRNA interaction matrix and miRNA-disease interaction matrix. Following this, the nonlinear features of diseases and lncRNAs are extracted using a graph attention auto-encoder, which largely retains the critical information and effectively aggregates the neighborhood information of nodes. In the end, LDAs can be predicted by fusing the linear and nonlinear characteristics of diseases and lncRNA. The HGATELDA model achieves an impressive AUC value of 0.9692 when evaluated using a 5-fold cross-validation indicating its superior performance in comparison to several recent prediction models. Meanwhile, the effectiveness of HGATELDA in identifying novel LDAs is further demonstrated by case studies. the HGATELDA model appears to be a viable computational model for predicting LDAs. | [
"['Jin-Xing Liu' 'Wen-Yu Xi' 'Ling-Yun Dai' 'Chun-Hou Zheng'\n 'Ying-Lian Gao']"
]
|
null | null | 2405.02356 | null | null | http://arxiv.org/pdf/2405.02356v1 | 2024-05-03T02:53:32Z | 2024-05-03T02:53:32Z | Stochastic Multivariate Universal-Radix Finite-State Machine: a
Theoretically and Practically Elegant Nonlinear Function Approximator | Nonlinearities are crucial for capturing complex input-output relationships especially in deep neural networks. However, nonlinear functions often incur various hardware and compute overheads. Meanwhile, stochastic computing (SC) has emerged as a promising approach to tackle this challenge by trading output precision for hardware simplicity. To this end, this paper proposes a first-of-its-kind stochastic multivariate universal-radix finite-state machine (SMURF) that harnesses SC for hardware-simplistic multivariate nonlinear function generation at high accuracy. We present the finite-state machine (FSM) architecture for SMURF, as well as analytical derivations of sampling gate coefficients for accurately approximating generic nonlinear functions. Experiments demonstrate the superiority of SMURF, requiring only 16.07% area and 14.45% power consumption of Taylor-series approximation, and merely 2.22% area of look-up table (LUT) schemes. | [
"['Xincheng Feng' 'Guodong Shen' 'Jianhao Hu' 'Meng Li' 'Ngai Wong']"
]
|
null | null | 2405.02357 | null | null | http://arxiv.org/pdf/2405.02357v1 | 2024-05-03T02:54:43Z | 2024-05-03T02:54:43Z | Large Language Models for Mobility in Transportation Systems: A Survey
on Forecasting Tasks | Mobility analysis is a crucial element in the research area of transportation systems. Forecasting traffic information offers a viable solution to address the conflict between increasing transportation demands and the limitations of transportation infrastructure. Predicting human travel is significant in aiding various transportation and urban management tasks, such as taxi dispatch and urban planning. Machine learning and deep learning methods are favored for their flexibility and accuracy. Nowadays, with the advent of large language models (LLMs), many researchers have combined these models with previous techniques or applied LLMs to directly predict future traffic information and human travel behaviors. However, there is a lack of comprehensive studies on how LLMs can contribute to this field. This survey explores existing approaches using LLMs for mobility forecasting problems. We provide a literature review concerning the forecasting applications within transportation systems, elucidating how researchers utilize LLMs, showcasing recent state-of-the-art advancements, and identifying the challenges that must be overcome to fully leverage LLMs in this domain. | [
"['Zijian Zhang' 'Yujie Sun' 'Zepu Wang' 'Yuqi Nie' 'Xiaobo Ma' 'Peng Sun'\n 'Ruolin Li']"
]
|
null | null | 2405.02358 | null | null | http://arxiv.org/pdf/2405.02358v2 | 2024-05-07T01:59:37Z | 2024-05-03T03:12:55Z | A Survey of Time Series Foundation Models: Generalizing Time Series
Representation with Large Language Model | Time series data are ubiquitous across various domains, making time series analysis critically important. Traditional time series models are task-specific, featuring singular functionality and limited generalization capacity. Recently, large language foundation models have unveiled their remarkable capabilities for cross-task transferability, zero-shot/few-shot learning, and decision-making explainability. This success has sparked interest in the exploration of foundation models to solve multiple time series challenges simultaneously. There are two main research lines, namely pre-training foundation models from scratch for time series and adapting large language foundation models for time series. They both contribute to the development of a unified model that is highly generalizable, versatile, and comprehensible for time series analysis. This survey offers a 3E analytical framework for comprehensive examination of related research. Specifically, we examine existing works from three dimensions, namely Effectiveness, Efficiency and Explainability. In each dimension, we focus on discussing how related works devise tailored solution by considering unique challenges in the realm of time series. Furthermore, we provide a domain taxonomy to help followers keep up with the domain-specific advancements. In addition, we introduce extensive resources to facilitate the field's development, including datasets, open-source, time series libraries. A GitHub repository is also maintained for resource updates (https://github.com/start2020/Awesome-TimeSeries-LLM-FM). | [
"['Jiexia Ye' 'Weiqi Zhang' 'Ke Yi' 'Yongzi Yu' 'Ziyue Li' 'Jia Li'\n 'Fugee Tsung']"
]
|
null | null | 2405.02359 | null | null | http://arxiv.org/abs/2405.02359v1 | 2024-05-03T03:31:00Z | 2024-05-03T03:31:00Z | CVTGAD: Simplified Transformer with Cross-View Attention for
Unsupervised Graph-level Anomaly Detection | Unsupervised graph-level anomaly detection (UGAD) has received remarkable performance in various critical disciplines, such as chemistry analysis and bioinformatics. Existing UGAD paradigms often adopt data augmentation techniques to construct multiple views, and then employ different strategies to obtain representations from different views for jointly conducting UGAD. However, most previous works only considered the relationship between nodes/graphs from a limited receptive field, resulting in some key structure patterns and feature information being neglected. In addition, most existing methods consider different views separately in a parallel manner, which is not able to explore the inter-relationship across different views directly. Thus, a method with a larger receptive field that can explore the inter-relationship across different views directly is in need. In this paper, we propose a novel Simplified Transformer with Cross-View Attention for Unsupervised Graph-level Anomaly Detection, namely, CVTGAD. To increase the receptive field, we construct a simplified transformer-based module, exploiting the relationship between nodes/graphs from both intra-graph and inter-graph perspectives. Furthermore, we design a cross-view attention mechanism to directly exploit the view co-occurrence between different views, bridging the inter-view gap at node level and graph level. To the best of our knowledge, this is the first work to apply transformer and cross attention to UGAD, which realizes graph neural network and transformer working collaboratively. Extensive experiments on 15 real-world datasets of 3 fields demonstrate the superiority of CVTGAD on the UGAD task. The code is available at url{https://github.com/jindongli-Ai/CVTGAD}. | [
"['Jindong Li' 'Qianli Xing' 'Qi Wang' 'Yi Chang']"
]
|
null | null | 2405.02360 | null | null | http://arxiv.org/pdf/2405.02360v1 | 2024-05-03T03:39:26Z | 2024-05-03T03:39:26Z | Holistic Evaluation Metrics: Use Case Sensitive Evaluation Metrics for
Federated Learning | A large number of federated learning (FL) algorithms have been proposed for different applications and from varying perspectives. However, the evaluation of such approaches often relies on a single metric (e.g., accuracy). Such a practice fails to account for the unique demands and diverse requirements of different use cases. Thus, how to comprehensively evaluate an FL algorithm and determine the most suitable candidate for a designated use case remains an open question. To mitigate this research gap, we introduce the Holistic Evaluation Metrics (HEM) for FL in this work. Specifically, we collectively focus on three primary use cases, which are Internet of Things (IoT), smart devices, and institutions. The evaluation metric encompasses various aspects including accuracy, convergence, computational efficiency, fairness, and personalization. We then assign a respective importance vector for each use case, reflecting their distinct performance requirements and priorities. The HEM index is finally generated by integrating these metric components with their respective importance vectors. Through evaluating different FL algorithms in these three prevalent use cases, our experimental results demonstrate that HEM can effectively assess and identify the FL algorithms best suited to particular scenarios. We anticipate this work sheds light on the evaluation process for pragmatic FL algorithms in real-world applications. | [
"['Yanli Li' 'Jehad Ibrahim' 'Huaming Chen' 'Dong Yuan'\n 'Kim-Kwang Raymond Choo']"
]
|
null | null | 2405.02364 | null | null | http://arxiv.org/pdf/2405.02364v1 | 2024-05-03T06:32:07Z | 2024-05-03T06:32:07Z | A Survey on Contribution Evaluation in Vertical Federated Learning | Vertical Federated Learning (VFL) has emerged as a critical approach in machine learning to address privacy concerns associated with centralized data storage and processing. VFL facilitates collaboration among multiple entities with distinct feature sets on the same user population, enabling the joint training of predictive models without direct data sharing. A key aspect of VFL is the fair and accurate evaluation of each entity's contribution to the learning process. This is crucial for maintaining trust among participating entities, ensuring equitable resource sharing, and fostering a sustainable collaboration framework. This paper provides a thorough review of contribution evaluation in VFL. We categorize the vast array of contribution evaluation techniques along the VFL lifecycle, granularity of evaluation, privacy considerations, and core computational methods. We also explore various tasks in VFL that involving contribution evaluation and analyze their required evaluation properties and relation to the VFL lifecycle phases. Finally, we present a vision for the future challenges of contribution evaluation in VFL. By providing a structured analysis of the current landscape and potential advancements, this paper aims to guide researchers and practitioners in the design and implementation of more effective, efficient, and privacy-centric VFL solutions. Relevant literature and open-source resources have been compiled and are being continuously updated at the GitHub repository: url{https://github.com/cuiyuebing/VFL_CE}. | [
"['Yue Cui' 'Chung-ju Huang' 'Yuzhu Zhang' 'Leye Wang' 'Lixin Fan'\n 'Xiaofang Zhou' 'Qiang Yang']"
]
|
null | null | 2405.02366 | null | null | http://arxiv.org/pdf/2405.02366v1 | 2024-05-03T06:48:53Z | 2024-05-03T06:48:53Z | Bayesian and Convolutional Networks for Hierarchical Morphological
Classification of Galaxies | This work is focused on the morphological classification of galaxies following the Hubble sequence in which the different classes are arranged in a hierarchy. The proposed method, BCNN, is composed of two main modules. First, a convolutional neural network (CNN) is trained with images of the different classes of galaxies (image augmentation is carried out to balance some classes); the CNN outputs the probability for each class of the hierarchy, and its outputs/predictions feed the second module. The second module consists of a Bayesian network that represents the hierarchy and helps to improve the prediction accuracy by combining the predictions of the first phase while maintaining the hierarchical constraint (in a hierarchy, an instance associated with a node must be associated to all its ancestors), through probabilistic inference over the Bayesian network so that a consistent prediction is obtained. Different images from the Hubble telescope have been collected and labeled by experts, which are used to perform the experiments. The results show that BCNN performed better than several CNNs in multiple evaluation measures, reaching the next scores: 67% in exact match, 78% in accuracy, and 83% in hierarchical F-measure. | [
"['Jonathan Serrano-Pérez' 'Raquel Díaz Hernández' 'L. Enrique Sucar']"
]
|
null | null | 2405.02367 | null | null | http://arxiv.org/pdf/2405.02367v2 | 2024-05-08T10:47:28Z | 2024-05-03T07:37:50Z | Enhancing Social Media Post Popularity Prediction with Visual Content | Our study presents a framework for predicting image-based social media content popularity that focuses on addressing complex image information and a hierarchical data structure. We utilize the Google Cloud Vision API to effectively extract key image and color information from users' postings, achieving 6.8% higher accuracy compared to using non-image covariates alone. For prediction, we explore a wide range of prediction models, including Linear Mixed Model, Support Vector Regression, Multi-layer Perceptron, Random Forest, and XGBoost, with linear regression as the benchmark. Our comparative study demonstrates that models that are capable of capturing the underlying nonlinear interactions between covariates outperform other methods. | [
"['Dahyun Jeong' 'Hyelim Son' 'Yunjin Choi' 'Keunwoo Kim']"
]
|
null | null | 2405.02369 | null | null | http://arxiv.org/pdf/2405.02369v1 | 2024-05-03T09:12:46Z | 2024-05-03T09:12:46Z | No One-Size-Fits-All Neurons: Task-based Neurons for Artificial Neural
Networks | Biologically, the brain does not rely on a single type of neuron that universally functions in all aspects. Instead, it acts as a sophisticated designer of task-based neurons. In this study, we address the following question: since the human brain is a task-based neuron user, can the artificial network design go from the task-based architecture design to the task-based neuron design? Since methodologically there are no one-size-fits-all neurons, given the same structure, task-based neurons can enhance the feature representation ability relative to the existing universal neurons due to the intrinsic inductive bias for the task. Specifically, we propose a two-step framework for prototyping task-based neurons. First, symbolic regression is used to identify optimal formulas that fit input data by utilizing base functions such as logarithmic, trigonometric, and exponential functions. We introduce vectorized symbolic regression that stacks all variables in a vector and regularizes each input variable to perform the same computation, which can expedite the regression speed, facilitate parallel computation, and avoid overfitting. Second, we parameterize the acquired elementary formula to make parameters learnable, which serves as the aggregation function of the neuron. The activation functions such as ReLU and the sigmoidal functions remain the same because they have proven to be good. Empirically, experimental results on synthetic data, classic benchmarks, and real-world applications show that the proposed task-based neuron design is not only feasible but also delivers competitive performance over other state-of-the-art models. | [
"['Feng-Lei Fan' 'Meng Wang' 'Hang-Cheng Dong' 'Jianwei Ma' 'Tieyong Zeng']"
]
|
null | null | 2405.02371 | null | null | http://arxiv.org/pdf/2405.02371v1 | 2024-05-03T09:36:16Z | 2024-05-03T09:36:16Z | Architecture of a Cortex Inspired Hierarchical Event Recaller | This paper proposes a new approach to Machine Learning (ML) that focuses on unsupervised continuous context-dependent learning of complex patterns. Although the proposal is partly inspired by some of the current knowledge about the structural and functional properties of the mammalian brain, we do not claim that biological systems work in an analogous way (nor the opposite). Based on some properties of the cerebellar cortex and adjacent structures, a proposal suitable for practical problems is presented. A synthetic structure capable of identifying and predicting complex temporal series will be defined and experimentally tested. The system relies heavily on prediction to help identify and learn patterns based on previously acquired contextual knowledge. As a proof of concept, the proposed system is shown to be able to learn, identify and predict a remarkably complex temporal series such as human speech, with no prior knowledge. From raw data, without any adaptation in the core algorithm, the system is able to identify certain speech structures from a set of Spanish sentences. Unlike conventional ML, the proposal can learn with a reduced training set. Although the idea can be applied to a constrained problem, such as the detection of unknown vocabulary in a speech, it could be used in more applications, such as vision, or (by incorporating the missing biological periphery) fit into other ML techniques. Given the trivial computational primitives used, a potential hardware implementation will be remarkably frugal. Coincidentally, the proposed model not only conforms to a plausible functional framework for biological systems but may also explain many elusive cognitive phenomena. | [
"['Valentin Puente Varona']"
]
|
null | null | 2405.02372 | null | null | http://arxiv.org/pdf/2405.02372v2 | 2024-06-04T11:40:50Z | 2024-05-03T10:10:11Z | Triadic-OCD: Asynchronous Online Change Detection with Provable
Robustness, Optimality, and Convergence | The primary goal of online change detection (OCD) is to promptly identify changes in the data stream. OCD problem find a wide variety of applications in diverse areas, e.g., security detection in smart grids and intrusion detection in communication networks. Prior research usually assumes precise knowledge of the system parameters. Nevertheless, this presumption often proves unattainable in practical scenarios due to factors such as estimation errors, system updates, etc. This paper aims to take the first attempt to develop a triadic-OCD framework with certifiable robustness, provable optimality, and guaranteed convergence. In addition, the proposed triadic-OCD algorithm can be realized in a fully asynchronous distributed manner, easing the necessity of transmitting the data to a single server. This asynchronous mechanism could also mitigate the straggler issue that faced by traditional synchronous algorithm. Moreover, the non-asymptotic convergence property of Triadic-OCD is theoretically analyzed, and its iteration complexity to achieve an $epsilon$-optimal point is derived. Extensive experiments have been conducted to elucidate the effectiveness of the proposed method. | [
"['Yancheng Huang' 'Kai Yang' 'Zelin Zhu' 'Leian Chen']"
]
|
null | null | 2405.02373 | null | null | http://arxiv.org/pdf/2405.02373v1 | 2024-05-03T10:12:40Z | 2024-05-03T10:12:40Z | Exponentially Weighted Algorithm for Online Network Resource Allocation
with Long-Term Constraints | This paper studies an online optimal resource reservation problem in communication networks with job transfers where the goal is to minimize the reservation cost while maintaining the blocking cost under a certain budget limit. To tackle this problem, we propose a novel algorithm based on a randomized exponentially weighted method that encompasses long-term constraints. We then analyze the performance of our algorithm by establishing an upper bound for the associated regret and the cumulative constraint violations. Finally, we present numerical experiments where we compare the performance of our algorithm with those of reinforcement learning where we show that our algorithm surpasses it. | [
"['Ahmed Sid-Ali' 'Ioannis Lambadaris' 'Yiqiang Q. Zhao' 'Gennady Shaikhet'\n 'Amirhossein Asgharnia']"
]
|
null | null | 2405.02374 | null | null | http://arxiv.org/pdf/2405.02374v1 | 2024-05-03T10:33:19Z | 2024-05-03T10:33:19Z | Protein binding affinity prediction under multiple substitutions
applying eGNNs on Residue and Atomic graphs combined with Language model
information: eGRAL | Protein-protein interactions (PPIs) play a crucial role in numerous biological processes. Developing methods that predict binding affinity changes under substitution mutations is fundamental for modelling and re-engineering biological systems. Deep learning is increasingly recognized as a powerful tool capable of bridging the gap between in-silico predictions and in-vitro observations. With this contribution, we propose eGRAL, a novel SE(3) equivariant graph neural network (eGNN) architecture designed for predicting binding affinity changes from multiple amino acid substitutions in protein complexes. eGRAL leverages residue, atomic and evolutionary scales, thanks to features extracted from protein large language models. To address the limited availability of large-scale affinity assays with structural information, we generate a simulated dataset comprising approximately 500,000 data points. Our model is pre-trained on this dataset, then fine-tuned and tested on experimental data. | [
"['Arturo Fiorellini-Bernardis' 'Sebastien Boyer' 'Christoph Brunken'\n 'Bakary Diallo' 'Karim Beguir' 'Nicolas Lopez-Carranza' 'Oliver Bent']"
]
|
null | null | 2405.02375 | null | null | http://arxiv.org/pdf/2405.02375v2 | 2024-05-11T04:40:53Z | 2024-05-03T11:06:10Z | The Sparse Tsetlin Machine: Sparse Representation with Active Literals | This paper introduces the Sparse Tsetlin Machine (STM), a novel Tsetlin Machine (TM) that processes sparse data efficiently. Traditionally, the TM does not consider data characteristics such as sparsity, commonly seen in NLP applications and other bag-of-word-based representations. Consequently, a TM must initialize, store, and process a significant number of zero values, resulting in excessive memory usage and computational time. Previous attempts at creating a sparse TM have predominantly been unsuccessful, primarily due to their inability to identify which literals are sufficient for TM training. By introducing Active Literals (AL), the STM can focus exclusively on literals that actively contribute to the current data representation, significantly decreasing memory footprint and computational time while demonstrating competitive classification performance. | [
"['Sebastian Østby' 'Tobias M. Brambo' 'Sondre Glimsdal']"
]
|
null | null | 2405.02377 | null | null | http://arxiv.org/pdf/2405.02377v1 | 2024-05-03T12:14:48Z | 2024-05-03T12:14:48Z | Robustness of Decentralised Learning to Nodes and Data Disruption | In the vibrant landscape of AI research, decentralised learning is gaining momentum. Decentralised learning allows individual nodes to keep data locally where they are generated and to share knowledge extracted from local data among themselves through an interactive process of collaborative refinement. This paradigm supports scenarios where data cannot leave local nodes due to privacy or sovereignty reasons or real-time constraints imposing proximity of models to locations where inference has to be carried out. The distributed nature of decentralised learning implies significant new research challenges with respect to centralised learning. Among them, in this paper, we focus on robustness issues. Specifically, we study the effect of nodes' disruption on the collective learning process. Assuming a given percentage of "central" nodes disappear from the network, we focus on different cases, characterised by (i) different distributions of data across nodes and (ii) different times when disruption occurs with respect to the start of the collaborative learning task. Through these configurations, we are able to show the non-trivial interplay between the properties of the network connecting nodes, the persistence of knowledge acquired collectively before disruption or lack thereof, and the effect of data availability pre- and post-disruption. Our results show that decentralised learning processes are remarkably robust to network disruption. As long as even minimum amounts of data remain available somewhere in the network, the learning process is able to recover from disruptions and achieve significant classification accuracy. This clearly varies depending on the remaining connectivity after disruption, but we show that even nodes that remain completely isolated can retain significant knowledge acquired before the disruption. | [
"['Luigi Palmieri' 'Chiara Boldrini' 'Lorenzo Valerio' 'Andrea Passarella'\n 'Marco Conti' 'János Kertész']"
]
|
null | null | 2405.02383 | null | null | http://arxiv.org/pdf/2405.02383v1 | 2024-05-03T15:47:32Z | 2024-05-03T15:47:32Z | A Fresh Look at Sanity Checks for Saliency Maps | The Model Parameter Randomisation Test (MPRT) is highly recognised in the eXplainable Artificial Intelligence (XAI) community due to its fundamental evaluative criterion: explanations should be sensitive to the parameters of the model they seek to explain. However, recent studies have raised several methodological concerns for the empirical interpretation of MPRT. In response, we propose two modifications to the original test: Smooth MPRT and Efficient MPRT. The former reduces the impact of noise on evaluation outcomes via sampling, while the latter avoids the need for biased similarity measurements by re-interpreting the test through the increase in explanation complexity after full model randomisation. Our experiments show that these modifications enhance the metric reliability, facilitating a more trustworthy deployment of explanation methods. | [
"['Anna Hedström' 'Leander Weber' 'Sebastian Lapuschkin' 'Marina Höhne']"
]
|
null | null | 2405.02384 | null | null | http://arxiv.org/pdf/2405.02384v1 | 2024-05-03T15:54:50Z | 2024-05-03T15:54:50Z | CogDPM: Diffusion Probabilistic Models via Cognitive Predictive Coding | Predictive Coding (PC) is a theoretical framework in cognitive science suggesting that the human brain processes cognition through spatiotemporal prediction of the visual world. Existing studies have developed spatiotemporal prediction neural networks based on the PC theory, emulating its two core mechanisms: Correcting predictions from residuals and hierarchical learning. However, these models do not show the enhancement of prediction skills on real-world forecasting tasks and ignore the Precision Weighting mechanism of PC theory. The precision weighting mechanism posits that the brain allocates more attention to signals with lower precision, contributing to the cognitive ability of human brains. This work introduces the Cognitive Diffusion Probabilistic Models (CogDPM), which demonstrate the connection between diffusion probabilistic models and PC theory. CogDPM features a precision estimation method based on the hierarchical sampling capabilities of diffusion models and weight the guidance with precision weights estimated by the inherent property of diffusion models. We experimentally show that the precision weights effectively estimate the data predictability. We apply CogDPM to real-world prediction tasks using the United Kindom precipitation and ERA surface wind datasets. Our results demonstrate that CogDPM outperforms both existing domain-specific operational models and general deep prediction models by providing more proficient forecasting. | [
"['Kaiyuan Chen' 'Xingzhuo Guo' 'Yu Zhang' 'Jianmin Wang' 'Mingsheng Long']"
]
|
null | null | 2405.02385 | null | null | http://arxiv.org/pdf/2405.02385v2 | 2024-05-17T17:13:30Z | 2024-05-03T17:21:13Z | Efficient Deep Learning with Decorrelated Backpropagation | The backpropagation algorithm remains the dominant and most successful method for training deep neural networks (DNNs). At the same time, training DNNs at scale comes at a significant computational cost and therefore a high carbon footprint. Converging evidence suggests that input decorrelation may speed up deep learning. However, to date, this has not yet translated into substantial improvements in training efficiency in large-scale DNNs. This is mainly caused by the challenge of enforcing fast and stable network-wide decorrelation. Here, we show for the first time that much more efficient training of very deep neural networks using decorrelated backpropagation is feasible. To achieve this goal we made use of a novel algorithm which induces network-wide input decorrelation using minimal computational overhead. By combining this algorithm with careful optimizations, we obtain a more than two-fold speed-up and higher test accuracy compared to backpropagation when training a 18-layer deep residual network. This demonstrates that decorrelation provides exciting prospects for efficient deep learning at scale. | [
"['Sander Dalm' 'Joshua Offergeld' 'Nasir Ahmad' 'Marcel van Gerven']"
]
|
null | null | 2405.02412 | null | null | http://arxiv.org/pdf/2405.02412v1 | 2024-05-03T18:13:52Z | 2024-05-03T18:13:52Z | Deep Learning and Transfer Learning Architectures for English Premier
League Player Performance Forecasting | This paper presents a groundbreaking model for forecasting English Premier League (EPL) player performance using convolutional neural networks (CNNs). We evaluate Ridge regression, LightGBM and CNNs on the task of predicting upcoming player FPL score based on historical FPL data over the previous weeks. Our baseline models, Ridge regression and LightGBM, achieve solid performance and emphasize the importance of recent FPL points, influence, creativity, threat, and playtime in predicting EPL player performances. Our optimal CNN architecture achieves better performance with fewer input features and even outperforms the best previous EPL player performance forecasting models in the literature. The optimal CNN architecture also achieves very strong Spearman correlation with player rankings, indicating its strong implications for supporting the development of FPL artificial intelligence (AI) Agents and providing analysis for FPL managers. We additionally perform transfer learning experiments on soccer news data collected from The Guardian, for the same task of predicting upcoming player score, but do not identify a strong predictive signal in natural language news texts, achieving worse performance compared to both the CNN and baseline models. Overall, our CNN-based approach marks a significant advancement in EPL player performance forecasting and lays the foundation for transfer learning to other EPL prediction tasks such as win-loss odds for sports betting and the development of cutting-edge FPL AI Agents. | [
"['Daniel Frees' 'Pranav Ravella' 'Charlie Zhang']"
]
|
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