Fault Detection and Control Systems

Últimos artículos

• Machine Learning for Quantifier Selection in cvc5

  Jan Jakub\r{u}v, Mikol\'a\v{s} Janota, Jelle Piepenbrock, Josef Urban · 12 de diciembre de 2025

  In this work we considerably improve the state-of-the-art SMT solving on first-order quantified problems by efficient machine learning guidance of quantifier selection. Quantifiers represent a significant challenge for SMT and are technically a source of undecidability. In our approach, we train an …

• Emergent Granger Causality in Neural Networks: Can Prediction Alone Reveal Structure?

  Malik Shahid Sultan, Hernando Ombao, Maurizio Filippone · 9 de diciembre de 2025

  Granger Causality (GC) offers an elegant statistical framework to study the association between multivariate time series data. Vector autoregressive models (VAR) are simple and easy to fit, but have limited application because of their inherent inability to capture more complex (e.g., non-linear) as…

• Machine Unlearning via Information Theoretic Regularization

  Shizhou Xu, Thomas Strohmer · 3 de diciembre de 2025

  How can we effectively remove or ''unlearn'' undesirable information, such as specific features or the influence of individual data points, from a learning outcome while minimizing utility loss and ensuring rigorous guarantees? We introduce a unified mathematical framework based on information-theor…

• Towards Hierarchical Multi-Agent Decision-Making for Uncertainty-Aware EV Charging

  Lo Pang-Yun Ting, Ali \c{S}enol, Huan-Yang Wang, Hsu-Chao Lai, Kun-Ta Chuang, Huan Liu · 2 de diciembre de 2025

  Recent advances in bidirectional EV charging and discharging systems have spurred interest in workplace applications. However, real-world deployments face various dynamic factors, such as fluctuating electricity prices and uncertain EV departure times, that hinder effective energy management. To add…

• From homeostasis to resource sharing: Biologically and economically aligned multi-objective multi-agent gridworld-based AI safety benchmarks

  Roland Pihlakas · 1 de diciembre de 2025

  Developing safe, aligned agentic AI systems requires comprehensive empirical testing, yet many existing benchmarks neglect crucial themes aligned with biology and economics, both time-tested fundamental sciences describing our needs and preferences. To address this gap, the present work focuses on i…

• Causal Representation Learning with Observational Grouping for CXR Classification

  Rajat Rasal, Avinash Kori, Ben Glocker · 20 de noviembre de 2025

  Identifiable causal representation learning seeks to uncover the true causal relationships underlying a data generation process. In medical imaging, this presents opportunities to improve the generalisability and robustness of task-specific latent features. This work introduces the concept of groupi…

• Automating RT Planning at Scale: High Quality Data For AI Training

  Riqiang Gao, Mamadou Diallo, Han Liu, Anthony Magliari, Jonathan Sackett, Wilko Verbakel, Sandra Meyers, Rafe Mcbeth, Masoud Zarepisheh, Simon Arberet, Martin Kraus, Florin C. Ghesu, Ali Kamen · 18 de noviembre de 2025

  Radiotherapy (RT) planning is complex, subjective, and time-intensive. Advances with artificial intelligence (AI) promise to improve its precision and efficiency, but progress is often limited by the scarcity of large, standardized datasets. To address this, we introduce the Automated Iterative RT P…

• Time-Series-Informed Closed-loop Learning for Sequential Decision Making and Control

  Sebastian Hirt, Lukas Theiner, Rolf Findeisen · 18 de noviembre de 2025

  Closed-loop performance of sequential decision making algorithms, such as model predictive control, depends strongly on the choice of controller parameters. Bayesian optimization allows learning of parameters from closed-loop experiments, but standard Bayesian optimization treats this as a black-box…

• Efficiently Computing Compact Formal Explanations

  Min Wu, Xiaofu Li, Haoze Wu, Clark Barrett · 18 de noviembre de 2025

  Building on VeriX (Verified eXplainability, arXiv:2212.01051), a system for producing optimal verified explanations for machine learning models, we present VeriX+, which significantly improves both the size and the generation time of formal explanations. We introduce a bound propagation-based sensit…

• Temporal Test-Time Adaptation with State-Space Models

  Mona Schirmer, Dan Zhang, Eric Nalisnick · 18 de noviembre de 2025

  Distribution shifts between training and test data are inevitable over the lifecycle of a deployed model, leading to performance decay. Adapting a model on test samples can help mitigate this drop in performance. However, most test-time adaptation methods have focused on synthetic corruption shifts,…

• Uncertainty Quantification for Deep Learning

  Peter Jan van Leeuwen, J. Christine Chiu, C. Kevin Yang · 18 de noviembre de 2025

  We present a critical survey on the consistency of uncertainty quantification used in deep learning and highlight partial uncertainty coverage and many inconsistencies. We then provide a comprehensive and statistically consistent framework for uncertainty quantification in deep learning that account…

• Enhancing failure prediction in nuclear industry: Hybridization of knowledge- and data-driven techniques

  Amaratou Mahamadou Saley, Thierry Moyaux, A\"icha Sekhari, Vincent Cheutet, Jean-Baptiste Danielou · 18 de noviembre de 2025

  The convergence of the Internet of Things (IoT) and Industry 4.0 has significantly enhanced data-driven methodologies within the nuclear industry, notably enhancing safety and economic efficiency. This advancement challenges the precise prediction of future maintenance needs for assets, which is cru…

• Bayesian ICA with super-Gaussian Source Priors

  Jyotishka Datta, Soham Ghosh, Nicholas G. Polson · 17 de noviembre de 2025

  Independent Component Analysis (ICA) plays a central role in modern machine learning as a flexible framework for feature extraction. We introduce a horseshoe-type prior with a latent Polya-Gamma scale mixture representation, yielding scalable algorithms for both point estimation via expectation-maxi…

• Torch-Uncertainty: A Deep Learning Framework for Uncertainty Quantification

  Adrien Lafage, Olivier Laurent, Firas Gabetni, Gianni Franchi · 14 de noviembre de 2025

  Deep Neural Networks (DNNs) have demonstrated remarkable performance across various domains, including computer vision and natural language processing. However, they often struggle to accurately quantify the uncertainty of their predictions, limiting their broader adoption in critical real-world app…

• A Reliable Cryptographic Framework for Empirical Machine Unlearning Evaluation

  Yiwen Tu, Pingbang Hu, Jiaqi Ma · 6 de noviembre de 2025

  Machine unlearning updates machine learning models to remove information from specific training samples, complying with data protection regulations that allow individuals to request the removal of their personal data. Despite the recent development of numerous unlearning algorithms, reliable evaluat…

• Generalization Error Analysis for Selective State-Space Models Through the Lens of Attention

  Arya Honarpisheh, Mustafa Bozdag, Octavia Camps, Mario Sznaier · 5 de noviembre de 2025

  State-space models (SSMs) have recently emerged as a compelling alternative to Transformers for sequence modeling tasks. This paper presents a theoretical generalization analysis of selective SSMs, the core architectural component behind the Mamba model. We derive a novel covering number-based gener…

• A Three-Stage Bayesian Transfer Learning Framework to Improve Predictions in Data-Scarce Domains

  Aidan Furlong, Robert Salko, Xingang Zhao, Xu Wu · 31 de octubre de 2025

  The use of ML in engineering has grown steadily to support a wide array of applications. Among these methods, deep neural networks have been widely adopted due to their performance and accessibility, but they require large, high-quality datasets. Experimental data are often sparse, noisy, or insuffi…

• Probabilistic Kernel Function for Fast Angle Testing

  Kejing Lu, Chuan Xiao, Yoshiharu Ishikawa · 30 de octubre de 2025

  In this paper, we study the angle testing problem in the context of similarity search in high-dimensional Euclidean spaces and propose two projection-based probabilistic kernel functions, one designed for angle comparison and the other for angle thresholding. Unlike existing approaches that rely on …

• Pairwise Optimal Transports for Training All-to-All Flow-Based Condition Transfer Model

  Kotaro Ikeda, Masanori Koyama, Jinzhe Zhang, Kohei Hayashi, Kenji Fukumizu · 29 de octubre de 2025

  In this paper, we propose a flow-based method for learning all-to-all transfer maps among conditional distributions that approximates pairwise optimal transport. The proposed method addresses the challenge of handling the case of continuous conditions, which often involve a large set of conditions w…

• Causal Convolutional Neural Networks as Finite Impulse Response Filters

  Kiran Bacsa, Wei Liu, Xudong Jian, Huangbin Liang, Eleni Chatzi · 29 de octubre de 2025

  This study investigates the behavior of Causal Convolutional Neural Networks (CNNs) with quasi-linear activation functions when applied to time-series data characterized by multimodal frequency content. We demonstrate that, once trained, such networks exhibit properties analogous to Finite Impulse R…

• Prognostic Framework for Robotic Manipulators Operating Under Dynamic Task Severities

  Ayush Mohanty, Jason Dekarske, Stephen K. Robinson, Sanjay Joshi, Nagi Gebraeel · 28 de octubre de 2025

  Robotic manipulators are critical in many applications but are known to degrade over time. This degradation is influenced by the nature of the tasks performed by the robot. Tasks with higher severity, such as handling heavy payloads, can accelerate the degradation process. One way this degradation i…

• Conditional Mean and Variance Estimation via \textit{k}-NN Algorithm with Automated Variance Selection

  Marcos Matabuena, Juan C. Vidal, Oscar Hernan Madrid Padilla, Jukka-Pekka Onnela · 28 de octubre de 2025

  We introduce a novel \textit{k}-nearest neighbor (\textit{k}-NN) regression method for joint estimation of the conditional mean and variance. The proposed algorithm preserves the computational efficiency and manifold-learning capabilities of classical non-parametric \textit{k}-NN models, while integ…

• Smart Sensor Placement: A Correlation-Aware Attribution Framework (CAAF) for Real-world Data Modeling

  Sze Chai Leung, Di Zhou, H. Jane Bae · 28 de octubre de 2025

  Optimal sensor placement (OSP) is critical for efficient, accurate monitoring, control, and inference in complex real-world systems. We propose a machine-learning-based feature attribution framework to identify OSP for the prediction of quantities of interest. Feature attribution quantifies input co…

• Frequentist Validity of Epistemic Uncertainty Estimators

  Anchit Jain, Stephen Bates · 28 de octubre de 2025

  Decomposing prediction uncertainty into its aleatoric (irreducible) and epistemic (reducible) components is critical for the development and deployment of machine learning systems. A popular, principled measure for epistemic uncertainty is the mutual information between the response variable and mod…

• RAPTOR-GEN: RApid PosTeriOR GENerator for Bayesian Learning in Biomanufacturing

  Wandi Xu, Wei Xie · 27 de octubre de 2025