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Machine Learning
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Newest papers, page 192
TriRouter: Sparse-to-Dense Electrode-Space Routing for Robust EEG Motor Imagery Decoding
Motor imagery brain-computer interfaces require reliable decoding from multichannel EEG, but practical acquisition often yields sparse or corrupted electrode montages.HardEval: An Execution-Verified Benchmark for AI-Driven Security Hardening and Compliance Remediation
Implementing Security Technical Implementation Guide (STIG) controls remains a demanding operating-system administration task.Expressivity of IQP Feature-Map Quantum Kernels via Reproducing Kernel Hilbert Spaces
Quantum kernels are among the most promising methods in quantum machine learning, providing a natural way to embed data into high-dimensional Hilbert spaces via quantum circuits and capturing…Equivalence testing and run-count design for stochastic machine-learning experiments
Claims that two training methods perform equally well often rest on a non-significant difference between two or three runs per method.Blitz: Accelerating the Diffusion Modules of AlphaFold3 and Boltz-2
Diffusion-based protein structure prediction models produce all-atom structures in about 200 denoising steps, and that cost has driven a number of few-step samplers.Tool Recommendation for Multi-Step Agents: Finding Every Tool a Task Needs
A multi-step agent task fails when a single tool it needs is missing, yet the tools an agent may need are too many to list: the roughly 8,200 tools on the platform we study have definitions that…Neural Expansion: A Unified Mechanism for How Deep Neural Networks Generalize
Deep neural networks demonstrate remarkable generalization despite being highly over-parameterized, challenging traditional statistical learning theories.ECG-SDI: Single-lead Electrocardiogram Enables Continuous Sleep Depth Index Profiling and Scalable Digital Biomarkers for Sleep Health
Conventional sleep staging provides a coarse-grained representation that obscures within-stage depth variation.Escaping Parameter Space: Tight Generalization Bounds via Representation Quality
We derive a tight generalization bound for deterministic neural We derive a tight generalization bound for deterministic neural networks based on their last hidden layer representations.Bridging the Language Gap in Scholarly Data II: Enhancing Gender Inference for Chinese Names
Perceived gender inference from names is important for inclusive scientometrics research, yet existing methods perform poorly on non-Western scholars.LagNet: Lag-Structured Operators for Long-Term Time Series Forecasting
Long-term time series forecasting maps a finite history window to an extended future horizon.From Flow-Box Invariants to Global Conservation Laws: Machine Learning-Assisted Detection of Singularities and Near-Integrability
Conservation laws reveal invariant structure in dynamical systems and constrain their evolution to lower-dimensional level sets.Universal Semantic Enhancement for Weakly Supervised Anomaly Detection
Weakly supervised anomaly detection on tabular data is an essential task in many label-scarce applications, since tabular data is widely encountered and it is commonly observed in practice that only…Logic-Guided Graph Denoising Diffusion Models
Graph diffusion models are competitive approaches for generating complex graph-structured data.Mesoscopic learning dynamics of extensive-width neural networks
Understanding how stochastic gradient descent learns internal representations in wide neural networks trained on rich target functions is a central problem in machine learning.Sampling the XY model with an attention-based neural network
We present a transformer-based variational autoregressive network for sampling lattice systems with continuous degrees of freedom and apply it to the two-dimensional XY model near the…Parametric Resonance in Primal-Dual Learning for Slowly Non-Stationary Constrained MDPs
Primal-dual methods for constrained Markov decision processes are analyzed under a two-timescale assumption, with non-stationarity entering through two channels.Task-aware Kernel Stein certificates for Rényi differential privacy
Rènyi differential privacy, based on Rènyi divergence, has become a notion of choice for privacy guarantees.Explainable Machine Learning Analysis of Temperature Effects on Mechanical Strength of Fiber-Reinforced High-Performance Concrete
Predicting the compressive strength of fiber-reinforced high-performance concrete subjected to temperature exposure is challenging because to the intricate interdependencies of mixture composition…Learning Personalized Memory Gating from Delayed One-Sided Feedback
Long-term memory enables language-model agents to personalize future interactions, but a finite memory bank cannot preserve every extracted fact.