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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 29
Role-based Effectiveness of an Epistemic Uncertainty Estimate in Training Models for Semantic Segmentation of 3D LiDAR Point Clouds
Point cloud segmentation is a fundamental task in 3D data analysis, with applications across robotics, augmented and virtual reality, and remote sensing.Readable Is Not Steerable: Pre-Registered Tests of Physical Structure in Learned Fluid Representations
Self-supervised world models are often said to organize their latents around physical quantities.Multimodal Fusion Methods for Whole-Slide Images and Clinical Data in Inflammatory Bowel Disease
Multimodal AI fusion of histology and clinical data is an active research area in computational pathology, but fusion methods are rarely compared under identical conditions and across different…Learning vs. Optimizing Players in Budgeted Games
We study repeated general games over $T$ rounds between a utility-maximizing optimizer and a learning agent, where budget constraints couple the players' decisions across rounds.Exploring the Privacy--Size Tradeoff for Differentially Private Self-Attentive Variational Autoencoders
Data tends to be most useful when it is shared, and the data most worth sharing is often the data that privacy obligations restrict.C-GRPO: Conformal Group Relative Policy Optimization
Reasoning-oriented language model training increasingly relies on multiple on-policy rollouts, but standard Group Relative Policy Optimization (GRPO) uses a fixed group size, wasting compute on easy…Distinguishing Initial-Condition and Model Error Using Machine-Learning Weather Models
Differentiable weather models allow atmospheric initial conditions to be directly optimized to shadow a known trajectory, which we take from a reanalysis dataset.GaussianRenderFormer: Transformer-based Rendering of 3D Gaussian Splats
Can a neural network learn to render 3D Gaussian splats on its own, with no rasterizer in the renderer?Relevance Propagation as Entropic Optimal Transport on Filippov Neural Networks: Sinkhorn Propagation and Region Relevance in Image Classifiers
Relevance propagation, which redistributes a network's prediction backward through the layers, is usually treated as a heuristic.RIDGE: State-Conditioned Reward Blending for Behavioral Coverage in Deep RL Game Agents
A deep RL agent trained on a fixed persona reward plays one way: an Explorer keeps exploring while starving, a Survivor hoards on an empty map.Transport along Local Annealing Paths: Wasserstein Geometry and Multimodal Sampling
Variational approaches to learning transport maps for multimodal sampling can suffer from mode collapse.On the Generalization Limitation of On-Policy Distillation for Post-RL Consolidation
Multi-teacher on-policy distillation (MOPD) extends on-policy distillation (OPD) to consolidate capabilities from multiple teachers.LACS: Distribution-Sensitive Label-Agnostic Confidence Selection for Dynamic Data Pruning
Training on large datasets motivates selecting which examples receive expensive training computation.Spatio-Temporal Multi-Order Interaction for Video Recognition
Many temporal approaches for video recognition rely on explicit cross-channel aggregation projections, which have quadratic complexity in the number of channels, introducing significant…Data-Efficient Scaling-Law Discovery for Recommendation Systems via Coupled Hyperparameter Scaling
Large-scale ranking and recommendation models of Web platforms benefit from increased compute much as large language models do, and their scaling behavior is broadly power-law in form.Quantifying Trait-State Hierarchy in EEG Foundation Model and Signal Processing Embeddings
Electroencephalography (EEG) measures brain activity through recording electrical potentials of the scalp.When Are Two SAE Latents the Same Feature?
Comparing sparse autoencoders (SAEs) requires connecting feature correspondence, differences between whole dictionaries, and uncertainty from finite evaluation data.Beyond Accuracy: Information-Theoretic Decoder Evaluation Under Latent Intent
A central challenge in assistive brain-to-robot control is decoding a user's latent intent from complex and noisy brain signals.Adaptive Domain Decomposition for Finite Basis Physics-Informed Neural Networks
In this work, we present adaptive domain decomposition finite basis physics-informed neural networks (ADD-FBPINNs).Revisiting Critic Estimation in Soft Actor-Critic Algorithms
Soft Actor-Critic (SAC) is a widely used deep reinforcement learning algorithm that combines entropy regularization with an actor-critic architecture.