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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 63
Accurate and Auditable ECG Classification via Visibility Graph Neural Networks
Physicians read electrocardiograms (ECGs) from waveform shape and timing, yet accurate deep-learning classifiers rarely show which waveform regions drive their predictions.On-device Detection of Wearer-induced NLOS on Chest-worn UWB Tags: Machine Learning, Link Indicators, or Both?
Ultra-wideband (UWB) enables high-accuracy distance estimation (ranging), but performance degrades in non-line-of-sight (NLOS) conditions.Towards Out-of-Distribution Uncertainty Quantification for Machine-Learned Interatomic Potentials via Pre-Training
Informative uncertainty quantification (UQ) is essential for the practical deployment of machine-learned interatomic potentials (MLIPs), as it provides insight into their reliability across chemical…What Standard Evaluations Hide About Detectors of Hijacked LLM Agents: Rare Attacks, Unseen Tools and Benchmark Transfer
Detectors can flag hijacked large language model (LLM) agents from the traces of the tools they call.Backpropagation-free Deep Learning for Time-Series Classification
Deep neural networks for time series classification (TSC) are trained almost exclusively by error backpropagation, which imposes update locking and the weight-transport problem.Exploring Bad Minima in Transfer Learning
Transfer learning is a highly successful learning paradigm in deep learning which leverages knowledge from one task and applies it to another.Continual Reinforcement Learning Is an Integration Problem
This paper presents an opinionated set of ten desiderata intended to inform the design and evaluation of reinforcement-learning agents that learn continually throughout a continuing lifetime.DIFFNAS: Predictor-Free Neural Architecture Search via Diffusion Model and Zero-Cost Evaluation
Neural Architecture Search (NAS) has advanced the automated design of deep networks, but remains computationally expensive due to reliance on model training or performance predictors.ElTab: Elastically Looped Tabular Transformers
Tabular transformer models such as TabPFN achieve strong predictive performance but incur substantial inference costs, making them difficult to deploy in resource-constrained settings.Trainable Vector Quantization for Large Language Models
Vector Quantization (VQ) is a rapidly evolving technique for compressing Large Language Model (LLM) weights, reducing memory usage, and speeding up inference.Overcoming the Representational Limitations of Amplitude Encoding for Quantum Neural Networks
Quantum Neural Networks (QNNs) offer a framework for learning functions from classical data, but their expressive power depends critically on how inputs are encoded into quantum states.Text as an Index for Zero-Shot Cross-Domain Knowledge Tracing
Knowledge tracing (KT) predicts a student's next response from their history.Learning Nonlocal Interactions in Large-Scale Heterogeneous Metamaterials through Scientific Machine Learning
In multiscale systems, nonlocal interactions among spatially distributed constituents can unlock collective, potentially emergent behavior irreducible to individual constituent responses.On-Chain Behavioral Signatures for Classifying Centralized Exchange Addresses on Ethereum Using Proof-of-Reserves
Centralized exchanges (CEXs) are key gateways between traditional finance and digital asset markets, yet identifying their blockchain addresses remains challenging.On k-nearest-neighbor regression in metric spaces
We study mean squared consistency of k-nearest-neighbor regression for metric space valued features.Statistical Foundations of Reinforcement Learning: A Non-Asymptotic Perspective
Reinforcement learning (RL) embodies a foundational paradigm for sequential decision-making in the face of uncertainty.Variance Reduction for Decentralized Stochastic Optimization with Multiple Local Updates
Multiple local updates are widely used in decentralized optimization to enhance communication efficiency, often in conjunction with stochastic gradients when exact gradient computation is costly.Binary Sparse Codes: Where are we on Energy Efficient Representations?
Biological neural systems achieve impressive computational capabilities under tight metabolic constraints, relying on sparse cortical representations that minimise total neural activity.Observable-Specific Validity Tests for Scientific AI: Controlled Approximations and Intervention Response
An approximation can reproduce an energy while missing its response, or satisfy an error tolerance without being certified by a given bound.Optimizing Stochastic Policy Geometry for Few-Step Flow Reinforcement Learning
Few-step flow models generate samples through a handful of large denoising transitions.