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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 151
Ensemble-Aware Structural Encoding (EASE): Conditioning Protein Encoders on Peptide Conformational Ensembles
Protein foundation models provide transferable representations for downstream peptide prediction tasks.BCT: Bayes-Consistent Tempering for Flow Language Models
Temperature scaling is typically applied as a post-hoc transformation of model probabilities.CUA-Recovery: Benchmarking Error Recovery in Long-Horizon Computer-Use Agents
As computer-use agents (CUAs) move to long-horizon workflows spanning multiple applications, undetected errors can silently corrupt persistent state and cause cascading failures in subsequent actions.Inferring continuous cognitive traits from limited neuroimaging data using pairwise convolutional neural networks
Individual differences in cognitive abilities vary widely, yet the structural brain features supporting these differences remain poorly understood.Pairwise Learning Increases Data Efficiency in Deep Neural Networks
Deep neural networks often fail at small-sample regression, collapsing to low-variance predictions that miss individual differences.Neural Operator Splitting for Fluid Dynamics in Complex Geometry
Solving dynamical systems in complex and diverse geometries remains challenging for both numerical solvers and learning-based surrogate models.Hybrid Momentum Methods for Optimizing Deep Learning Models
Momentum is central to stochastic optimization in deep learning, including the training of large language models.CyberHull: Deterministic Malicious URL Isolation via Convex Hull
The emergence of increasingly sophisticated and evasion-resistant phishing attack strategies represents a significant threat to modern cybersecurity, particularly in cyber-physical systems and…Influence Is the Target-Response Jacobian Times Residuals
We introduce the target-response Jacobian $J = [\partial \hat y_i/\partial y_j]$, which measures how model predictions respond to changes in individual training targets. $J$ complements…Understanding Data Augmentation under Label Noise
Data augmentation (DA) has become a fundamental component of modern machine learning systems and is widely regarded as an effective strategy for improving robustness and generalization.Multi-Objective Evolutionary Semi-Supervised Learning for Resource-Aware Smart City Classification
Smart-city applications often combine scarce labeled data with abundant unlabeled observations.Privacy Accounting for Empirical Denoisers
Diffusion models have emerged as the foundation of modern generative systems, yet their high memorization capacity raises privacy concerns.When Does Experience Memory Help Closed-Loop LLM Driving Agents? A Controlled Evaluation with Resampled Warmup Pools, Token-Matched Fillers, and Audited Baselines
When does experience memory help a closed-loop LLM driving agent?Anytime-Valid Runtime Monitoring of Learned Barrier Certificates
Learned barrier certificates provide a principled mechanism for establishing safety of stochastic systems, but their validity may deteriorate during deployment when the underlying dynamics differ…EEG-Based Classification of Hypnotic Susceptibility Using Two-Dimensional Convolutional Neural Networks
Guided Imagery (GI) is a mind-based relaxation technique widely used in clinical and therapeutic settings.Constant Step Sizes for Muon-Type Spectral Optimizers on Strongly Convex Quadratics
Muon and related optimizers update a weight matrix with a spectrally transformed gradient.Detection of Memorization in Diffusion Models Through Thermodynamic Singularity
In diffusion models, proper distributional learning entails reversing a diffusion process over underlying density functions, whereas a key failure is memorization, in which the reverse process…Give the Model a Finger: Developmentally Aligned Evaluation of Counting in Vision-Language Models
Vision-language models (VLMs) are exact on small sets and drift into estimation on larger ones, a profile reminiscent of children before they master counting.AC/DC on a Budget - Alternating Sparse Phases
State-of-the-art neural network sparsification relies on alternating dense and sparse training phases.Foundation-Style Pretraining for Scalable and Transferable Distributed PV Forecasting
The rapid growth of distributed photovoltaic (PV) generation is increasing the demand for accurate forecasting across large numbers of geographically dispersed stations.