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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 182
Rethinking EEG Spatial Super-Resolution as Dynamic State Decomposition and Filtering
EEG spatial super-resolution aims to recover high-density neural signals from sparse electrode observations.Contrastive Hypergraph Source-free Domain Adaptive Object Detection in Adverse Weather Conditions--Extended Version
Adverse weather conditions such as fog, rain, snow, and low illumination introduce structured visibility degradations that severely impair object detectors trained on clear-weather data.Structured Latent Dynamics for Long-Horizon Embodied World Models
Reliable long-horizon prediction remains a central challenge for embodied world models.From Score to Diagnosis: Search-Independent Post-Diagnosis for Symbolic Regression
Symbolic regression (SR) solvers employ diverse search mechanisms, yet most rely on the same low-information scalar fitness.A Multi-Step Inertial ADMM Algorithm for Federated Learning
Federated learning has recently emerged as an important paradigm for large-scale distributed model training, which can be naturally formulated as a class of distributed learning problems.Write Once, Query Many: Evolving Compact Reusable Memory for Long Videos
Long videos often exceed the context capacity of multimodal large language models (MLLMs), while agentic search repeats costly video processing and grounding for each query.Accelerating Finite-Sum Bilevel Optimization with Fully First-Order Oracles
We study nonconvex--strongly-convex bilevel optimization with finite-sum objectives and fully first-order oracles.Theory-Guided Instance-Dependent Early Stopping via Second-Order Loss Dynamics
Instance-dependent early stopping (IES) reduces training computation by skipping backpropagation for examples whose losses have stabilized, but the second-order loss-difference criterion used by IES…From Stories to Features: Scaling Concept Vectors for AI Alignment & Interpretability
We study feature vectors: directions in a language model's activation space that correspond to human-interpretable concepts, such as honesty or deception.No-Regret Revenue Maximization in Sequential Market Making Under Inventory Constraints
Market makers keep trading efficient by setting bid and ask prices for buyers and sellers.Representations Have Trajectories: Characterizing Temporal Representation Dynamics in Neural Networks
Recent work has shown that deep neural networks exhibit an implicit low-rank simplicity bias.ASR Personalization in Pathological Voice: Accuracy, Efficiency, and Duration Effects
Automatic Speech recognition (ASR) struggles with Pathological speech due to persistent speaker-specific mismatch.DeliMF: Gated Cross-Attention Fusion of Structured Time Series and Clinical Text for Early Intensive Care Delirium Prediction
Delirium affects a large fraction of intensive care unit (ICU) patients and foreshadows longer stays, lasting cognitive decline, and higher mortality.Conditional Weight of Evidence for Gradient-Boosted Credit Models: Estimation, Additivity and Its Relation to Adverse Action Reasons
Traditional weight of evidence assigns a credit characteristic a value based on its association with default across a population.Training with Ciphers to Enhance In-Context Terminology Learning For Translation from Rare Languages
Fine-tuning LLMs with in-context examples (IC-Train) lets translation models adapt to new parallel data at deployment, but it is unclear whether they actually learn the translations of rare terms…AdamPM: Memory-Efficient LLM Optimization via Moment Pooling
Adam is widely used for training large language models (LLMs) owing to its strong optimization performance and training stability, but its element-wise estimation for the first and second moments…Severity-Channel Conditioning: An Injection-Level Study for Consumer-Device Deblurring
Motion-deblurring models lose substantial accuracy when transferred from professional cameras to smartphone-derived imagery.TCMScience: An Autonomous Scientist for Traditional Chinese Medicine
Traditional Chinese medicine (TCM) research connects classical texts, medicinal entities, molecular assays, computational predictions, and clinical studies.How Much Do Activation Monitors Benefit from Original Model Computations?
One powerful way to safeguard AI systems is to monitor their internal computations--where signs of undesirable behavior might be more easily detectable than from models' inputs and outputs alone.Uncovering Semantic Hierarchies in Text-Attributed Graphs via Variational EM-based LLM-GNN Synergy
While hierarchical structures are intrinsic to organizing human knowledge, current representation learning on text-attributed graphs predominantly operates on flat semantic spaces, overlooking the…