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.
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Newest papers, page 39
RangeFinder: Predicting Ligand Property Ranges from Protein Pockets
Identifying ligands for a protein target requires searching a vast chemical space for a comparatively small set of compatible molecules.A Geometry-Informed Neural Operator surrogate of LISFLOOD-FP for rapid pluvial-fluvial flood forecasting: the case of Forlì, Italy
Physically based two-dimensional hydraulic models are too slow for real-time inundation forecasting and large ensembles.Progressive Multi-Scale Super-Resolution via Cascaded Bicubic Anchors and Inter-Scale Mutual Refinement
Super-resolution networks are trained for one magnification factor, so several output resolutions require several models.Low-Rank Clebsch--Gordan Coupling for Equivariant Spherical Forecasting
Spherical-harmonic neural operators provide an efficient representation for global forecasting, but their discretized pointwise nonlinearities can break rotational equivariance through aliasing.Finite-Memory Control of POMDPs: Fundamental Limits and Efficient Design
Optimal control of partially observable Markov decision processes (POMDPs), in general, requires a complete history.Temporal Island Sparse Autoencoders for Interpreting Clinical Time-Series Models
Deep learning models for electronic health records achieve strong predictive performance but provide little insight into what clinical concepts they represent internally.A Living Leaderboard for Graph Machine Learning
Graph machine learning benchmarking results are scattered across papers that use quite different sets of datasets and baseline methods, making it difficult to tell whether a reported gain reflects…Bridging Neural Tangent Kernel Analysis and Reinforcement Learning for Deep Function Approximation
Despite their widespread success across machine learning, the learning dynamics and generalization properties of neural networks remain poorly understood.Playing Dumb: How a Chess Transformer Simulates Users Across Skill Levels
Generative models increasingly simulate users at different skill levels in education and AI evaluation.Subpolynomial-Query Proximal Sampling Beyond Log-Concavity
We study the gradient query complexity of sampling from a density $π(x)\propto\mathrm{e}^{-V(x)}$ on $\mathbb{R}^d$, where $V$ has a $β$-Lipschitz gradient but need not be convex.Error Accumulation in Second-Order Sequential Machine Unlearning
Machine unlearning seeks to remove the influence of deleted training data without full retraining.Improving Hamiltonian Learning through Local Energy Decomposition
Hamiltonian neural networks provide a principled framework for learning conservative dynamics.Online Class Posteriors for Interpretable and Controllable Discrete Diffusion
Discrete diffusion models remain far less understood than their continuous counterparts, despite recent breakthroughs across modalities, most notably in text.Unrolled gradients in disguise: bridging interpolation-based and Jacobian regularization for stable neural dynamics
Hybrid models and neural differential equations (NDEs) are becoming increasingly important for modelling physical systems; however, they often encounter stability and accuracy issues during…A Bayesian Framework for Safe Optimal Control of Unknown Systems from Input-Output Data
As control engineering is applied to increasingly complex systems, learning-based control, which uses empirical data to reduce the reliance on first-principles models, has emerged as a promising…Tilted Convexity and an Exact Offline-Online Separation
Convexity has three equivalent descriptions: support by affine functions, monotonicity of the subdifferential, and increasing secant slopes.Identifying the Shear Piezoelectric Coefficient using Physics-Informed Neural Networks under Exact Boundary and Initial Constraints
The shear piezoelectric coefficient $e_{15}$ governs the shear-mode electromechanical coupling in piezoelectric micromachined ultrasonic transducers (PMUTs) and surface acoustic wave (SAW) devices…A Hybrid Visitor Prediction Framework using Interpretable Temporal features, Integrating IQR and Anomaly Detection in Lakes in Nebraska
For effective resource management, infrastructure planning, and sustainable tourism, it is really important to predict accurate visitors.ST-GFM: Spectral-Temporal Graph Forecasting with Measured Perturbation Bounds and Masked Spectral Reconstruction
Graph forecasters for sensor networks are trained in one regime and deployed in others, where sensor coupling and signal frequency content both change.RASP: Read-All, Sparse-Plasticity Continual Learning in a Stationary-Size LoRA
Parameter-efficient continual learning on pre-trained Vision Transformers largely relies on a costly premise: preventing catastrophic forgetting requires either spawning a dedicated LoRA branch per…