AI & Machine Learning · Computer Science & AI
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.
Get the few papers worth your time in Machine Learning every morning.
Follow Machine Learning and ReadSearch reads its new papers each day, then brings you the few worth reading, each with its key finding in a sentence.
Newest papers, page 157
Two $\operatorname{ReLU}$ Layers is All You Need
We prove that every $\operatorname{ReLU}$ network can be exactly simulated by a $\operatorname{ReLU}$ network with two hidden layers.WildMAE: Structure-Aware Masked Autoencoding for Wildlife Image Reconstruction
Camera-trap imagery presents substantial variation in animal appearance, illumination, background clutter, and visual degradation.PaPi: Parallel Picard-Duhamel Integration for Fast Neuro-Spectral Architectures
Neuro-spectral architectures (NeuSA) are a class of physics-informed neural networks (PINNs) that have proven effective for solving time-dependent PDEs in the frequency domain.Neural Implicit Energy Optimization for LiDAR Mapping
Neural implicit representations provide memory-efficient signed distance field (SDF) parameterizations for large-scale LiDAR scenes, but the geometry recovered from sparse and often noisy inputs…Turning TS2Vec into a Foundation Model for Time Series Classification
TS2Vec is one of the strongest self-supervised methods for time series classification, introduced in 2022.Efficient Edge-aware Attention Network for Graph Generation
Graph generative models commonly use graph-specific Transformer architectures to jointly update node and edge features.Adaptive Multi-Token Prediction: How far should we go?
Multi-token prediction (MTP) can accelerate language-model decoding by drafting several tokens and verifying them in a single target-model pass.Planning under Noise with Neural Operators
Planning under uncertain control execution noise requires reliable feedback policies across diverse environments.Learning Adaptable Dexterous Skills via Hybrid Dexterous Mimicking
Dexterous multi-finger hands hold promise to unlock diverse skills like functional grasps, in-hand reorientation, and dynamic handover that the human-designed world assumes, yet existing learning…Joints, Camera, Action! Co-Generating 3D Motion and Video for Arbitrary Rigs
Video generation models can now depict remarkably realistic motion.Solar System Surface Technosignatures: Searching for Signs of Artificial Design on Extraterrestrial Terrain
Artificial radio emissions have historically been a major focus of technosignature searches.Interaction-Gated Time-Frequency Learning for Parameter-Efficient PPG-Based Heart Rate Estimation
Photoplethysmography (PPG) enables low-cost and non-invasive heart rate (HR) monitoring but remains sensitive to waveform distortion, physiological variability, and measurement interference.Scaling in Games: Continued Pre-Training For Embodied Agents In Diverse Virtual Worlds
Scaling data, diversity and model capacity has improved embodied learning in physical environments, where diverse experiences remain grounded in shared physical laws.Reinforcement learning for hardware acceleration and system-level optimization on FPGA and integrated-circuit platforms: A survey
Reinforcement learning (RL) enables adaptive and autonomous decision-making in embedded, edge, and heterogeneous computing systems.Scalable Stress-Constrained Topology Optimization: A Simultaneous Approach Based on Machine Learning
Large-scale topology optimization (TO) remains computationally demanding, especially for stress-constrained problems.SimHack: Predicting the onset of detectable reward hacking before RL training
When training LLMs to code with reinforcement learning, reward hacking is often detectable but difficult to prevent, since (a) environment hardening is increasingly difficult as training…Borrowing Strength Across Stores: Transfer Learning for Choice-Based Revenue Management
Large retail chains must tailor assortment and pricing decisions to local customer preferences, yet even retailers with extensive sales records often have limited data for estimating demand at the…STAR-XPC: Set Transformer for Estimating X-ray Transmission in Polymer Composites
Evaluating the energy transmission ratio of an X-ray beam through a polymer composite is a challenging task in radiation shielding studies.Big Claims, Small Margins: Do Reported GraphLLM Gains Survive Simple Baselines?
LLM-based methods for node classification on text-attributed graphs (TAGs) routinely report state-of-the-art accuracy.Offline Apprenticeship Learning for Human-Centric Environments
Effective decision-making in human-centric environments such as Intelligent Tutoring Systems (ITSs) and clinical healthcare requires policies that are adaptive, data-efficient, and ethically…