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Machine Learning
The newest papers in Machine Learning that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
The newest 5,000 papers, from 12 October 2026 to 5 November 2026: 5,000 arXiv preprints.
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Newest papers, page 23
Policy Network Diffusion
Diffusion models have shown remarkable progress in AI-generated content, including images and videos.Self-Privileged Fine-Tuning
Supervised fine-tuning (SFT) is a key component of language model post-training, enabling effective task adaptation and serving as an important foundation for subsequent reinforcement learning.Does LinUCB Need Valid Confidence Sets?
We show that replacing LinUCB's usual confidence width $β$ by any fixed $α>0$ preserves sublinear regret when the action set is $M$-smooth and $m$-strongly convex.Exploiting Biases in Attention for Transformers
Attention is known to be sparse in vision transformers.Leakage-free evaluation of residual graph convolutional networks for network intrusion detection
Graph neural networks are increasingly proposed for network intrusion detection, yet many reported results come from protocols in which test flows shape the graph used for training, duplicated flows…Best-of-Depth: Learning Exploratory Dynamics in Looped Transformers
Looped Transformers extend inference-time computation through repeated latent-state updates, but final-depth training does not directly encourage useful intermediate alternatives.Feature-Based and Deep Learning Methods for Astronomical Light Curve Classification
We compare feature-based machine learning and end-to-end deep learning for variable star classification using the Zwicky Transient Facility (ZTF) StarEmbed dataset, which contains about 40,000…FedCSGA: Evolutionary Client Selection with Joint Statistical and System Heterogeneity in Federated Learning
Federated Learning (FL) enables collaborative training of machine learning models across distributed clients while preserving data privacy.Sheaf-MPNN: A Library for Sheaf Neural Networks as Message Passing
Standard Graph Neural Networks (GNNs) implicitly assume that neighboring nodes share the same feature space, which contributes to oversmoothing and poor performance on heterophilic graphs.Uncertainty-Aware Prototype Learning for Open-Set Test-Time Adaptation
Open-set test-time adaptation (TTA) aims to adapt source-trained models to unlabeled test streams where covariate-shifted in-distribution (csID) samples and covariate-shifted out-of-distribution…Surrogate Models for Stochastic Al-Cu Phase-Field Solidification
Phase-field models resolve microstructural evolution during rapid alloy solidification, but their computational cost limits long trajectories, stochastic ensembles, and studies across process…One-shot Distributed Learning via Weighted Averaging with Server-side Information
Distributed learning has drawn considerable attention due to its capability of learning from distributed data without aggregating raw observations.DenseTwins: Dense Modality Decoupling for Multimodal Self-Supervision
Multimodal perception is central to many computer vision applications, including robotics and autonomous driving, where different sensing modalities provide complementary cues.Hybrid acoustic and pretrained embedding feature fusion for unmanned aerial vehicle detection using stacking ensemble learning under a leakage-aware evaluation protocol
Small multirotor drones produce a characteristic acoustic signature, and microphones offer a low-cost way to detect them.TAPES: Task-Aligned Joint Predictive Pretraining for Transaction Event Sequences
Transaction event sequences support diverse prediction tasks in online commerce and digital financial services.What Does It Mean to Remove an Edge? Intervention-Aware Fidelity for Graph Neural Network Explanations
Graph neural network explanations are often evaluated via deletion fidelity, i.e., by deleting their top-ranked edges and measuring the resulting change in model output.Non-Interfering Asymmetric AdamW Updates for Constrained Exploration of Equifinality in Hybrid Models
Hybrid models let neural networks supply the unresolved parameters of a process-based simulator, and are fitted to observations that constrain aggregate fluxes and storages but not the internal…Koopman Generative Operators for Efficient Probabilistic Time-Series Forecasting
Probabilistic time-series forecasting requires models that simultaneously capture structured temporal dynamics, expressive uncertainty, and efficient inference, yet existing approaches fall short of…Cooperative Multi-Agent Reinforcement Learning with Submodular Trajectory Rewards
We study cooperative multi-agent reinforcement learning with decoupled dynamics and a monotone submodular reward function over joint trajectories.LIPS-PC: Learning Illumination Patterns for Single-Pixel Classification
In single-pixel (ghost) imaging, known illumination patterns probe an object and a detector with no spatial resolution returns one number per pattern.