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Machine Learning
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The newest 5,000 papers, from 12 October 2026 to 5 November 2026: 5,000 arXiv preprints.
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Newest papers, page 73
Observability-Aligned Evidential Masked Pretraining for Reliable VRU Motion Prediction
Vulnerable road user (VRU) prediction must operate under partial observability and distribution shift, so reliable uncertainty matters alongside accuracy.A physics-informed uncertainty-aware surrogate for $p$-$n$ junction diodes
We develop an uncertainty-aware physics-informed surrogate for a one-dimensional $p$-$n$ junction diode, conditioned on device length, doping and bias.Enhancing Atrial Fibrillation Detection Through Hyperparameter Optimization
This study explores the role of hyperparameter optimization (HPO) in enhancing the detection of Atrial Fibrillation (AF) using both machine learning and deep learning models.How Much Should a VLA Trust Its Reasoning? Step-Level Guidance as a Measured Action-Value Function
Reasoning vision-language-action (VLA) policies write a reasoning trace and then decode the action from it.FIDE: Fiber-Informed Differentiable Experimental Design for Reference-Relative Law Dynamics
Aggregate measurements generally leave an evolving population law underdetermined, so different measurement systems can be equally adequate for a scientific task while constraining different aspects…Cheap Retrieval and Observation Credit Assignment from One Quantity: The Value of Information
Memory in reinforcement learning raises two problems.One Vector or Many? Clustered Optimal-Transport Steering under Quantization
Activation steering changes what a language model generates by editing its hidden states at inference time.Reinforcement Learning Under Runtime Confounding: Policy Learning and Off-Policy Evaluation
Reinforcement learning (RL) is increasingly used to train robust, adaptable systems across healthcare, e-commerce, and robotics.Coupled Guidance for Flow Matching
Guidance methods are essential tools for steering the generation process in diffusion models, improving the generalizability of stochastic robotic controllers and enabling high-quality controlled…D-MOD: Diffusion mode estimation for accelerated MRI
Diffusion models are powerful priors for accelerated MRI reconstruction, but most current methods are posterior samplers, which return a random draw from the posterior.Does System Size Matter in Uncertainty Quantification for Machine Learning Interatomic Potentials?
Machine-learning interatomic potentials (MLIPs) commonly use uncertainty estimates to flag out-of-distribution (OOD) configurations and select structures for active learning.Escaping Path Mirages in Offline Goal-Conditioned Reinforcement Learning
Offline goal-conditioned reinforcement learning remains challenging in stochastic long-horizon settings, where compounding value estimation errors hinder reliable goal reaching.A Deep Learning Framework for Quantile Demand Forecasting in E-commerce with Doubly Censored Data
In recent years, a growing number of online retail companies have embraced deep learning models for demand forecasting, leveraging their scalability in processing large-scale datasets.Mode Dynamics: A Framework for Neural Network Learning
We introduce Mode Dynamics, a framework that connects coordinated parameter motion during neural network learning to changes in model outputs.FARE: Factor-Aware Retrieval with Error-Guided Fusion for Retail Forecasting
Retail sales forecasting underpins inventory planning, yet promotions and calendar events reshape short-term demand.Fisher Contrastive Learning to Mitigate Feature Suppression: A Case Study in Acute Promyelocytic Leukemia Diagnosis
Self-supervised contrastive learning (SSCL) learns representations by contrasting augmented views of the same sample, but it can exhibit feature suppression, where dominant or easy-to-learn factors…Closing the Log Gap: Minimax-Optimal Regret for Episodic RL
In this paper, we study the regret minimization problem in episodic tabular MDPs.Hidden Disparities in Ranking: Category-Aware Fairness via Distributional Stability
We study the problem of fair ranking in recommender systems.Understanding axial attention in TSFMs through single location regression
Recent advances in Time Series Foundation Models (TSFMs) increasingly rely on axial attention mechanisms, but despite their empirical success, the theoretical properties of axial attention remain…DYNAS: A Dynamic Network Penetration Testing Framework Using Deep Hierarchical Reinforcement Learning
As network scale and uncertainty complicate automated penetration testing, reinforcement learning (RL) environments must support controlled evaluation under defensive and non-stationary conditions.