EMGFlow: Robust and Efficient Surface Electromyography Synthesis via Flow Matching
Flow matching approach for synthetic surface electromyography generation addressing data scarcity in gesture recognition with improved training stability.
Flow matching approach for synthetic surface electromyography generation addressing data scarcity in gesture recognition with improved training stability.
Machine learning classification pipeline using tree-based models to automatically detect and visualize glitches in gravitational-wave detector data.
Quantum computing analysis identifying necessary conditions for variational quantum circuits to reach exact ground states through group module constraints.
Parallel monitoring architecture for detecting and correcting reasoning degradation in multi-step LLM agents, reducing overhead to near-zero with novel probe-based approach.
Vision Transformer application for lymphoma morphological classification using weakly supervised training on small medical imaging datasets.
Ranking engine for daily fantasy sports recommendations optimized for time-sensitive match participation and engagement.
Machine learning method improving alignment between human and model visual perception through context-sensitive embedding representations.
Economic theory paper analyzing production network fragility and crisis generation in capitalist economies through evolutionary geometry.
Systematic study of synthetic data generation for LLM pretraining, testing rephrasing strategies, generator models, and source data across one trillion tokens to identify optimal design choices.
Adaptive conformal prediction method for improving factuality in LLM generations with prompt-dependent uncertainty estimates and statistical guarantees.
Exploration of masked and uniform-state diffusion language models for speech recognition rescoring and ASR hypothesis improvement.
Hierarchical RL with runtime safety shielding for automated power grid operations, addressing safety constraints and generalization to unseen topologies.
Comparative analysis of Fitted Dynamic Programming vs Reinforcement Learning for dynamic pricing across varying complexity levels and demand structures.
Multistage conditional compositional optimization framework combining stochastic programming with uncertainty handling. Applied to control and stopping problems.
Graph neural network with contrastive learning for sequential recommendation systems. Studies e-commerce user preference prediction.
Linear probe analysis of how LLMs internally represent rhetorical questions. Studies persuasive language understanding in neural representations.
Formalizes 'vibe-testing' methodology for LLM evaluation. Studies how practitioners informally assess models beyond benchmarks.
Optimal transport method for class centroid learning in online incremental learning with distribution shift. Addresses multimodal stream learning.
Automated feature preprocessing pipeline search for tabular machine learning. Studies AutoML approaches for classical model data preparation.
Hybrid attention model with feature decomposition for continuous glucose monitoring forecasting. Medical domain-specific time series prediction.
Ghost mechanism analytical model explaining abrupt learning in RNNs on memory tasks. Studies transient dynamics and computation timescales.
Federated learning framework analysis under wireless heterogeneity. Studies gradient aggregation strategies in distributed learning.
Markov decision processes with state sensing costs, balancing optimal actions against sensing/communication/computation expenses in decision-making.
Two-stage regularization-based structured pruning method for reducing LLM parameters while minimizing knowledge loss and retraining requirements.
Randomized Policy Learning approach for quadruped locomotion control with drastically reduced trainable parameters in neural network policies.
Minkowski weighted k-means++ for unsupervised feature selection in high-dimensional clustering by probabilistic centroid selection.
Token significance scoring in reinforcement learning to improve LLM reasoning efficiency by identifying which tokens contribute to correctness.
Biased Scan Attention Transformer Neural Processes for scalable spatiotemporal inference across geology, epidemiology, climate and robotics applications.
Multi-stage latent space dynamics identification framework for solving PDEs via data-driven reduced-order models using autoencoders and ODEs.
Theoretical analysis of CVaR policy evaluation limitations in MDPs using dual formulations.
Federated fine-tuning framework for multi-task adaptation in edge IoV networks with hierarchical rank scheduling.
Class-conditional heavy-tailed priors in VAEs addressing latent space bias for long-tailed generative modeling.
Guidance framework for discrete flow matching with exact guidance in discrete state spaces.
Local scoring method for selecting reasoning data from diverse teachers for efficient distillation into student models.
Numerically stable implementation of power transforms for data preprocessing with federated learning support.
Student learning model from 3.8M program traces analyzing coding skill development through interaction patterns.
Function-centric analysis of flat vs sharp minima in deep networks, showing sharpness is function-dependent.
Open-weight LLMs achieving IOI gold medal through test-time compute scaling for competitive programming.
Neural method with hyper-tour for targeted neighborhood search solving large-scale TSP instances efficiently.
Comprehensive review of Kolmogorov-Arnold Networks covering theory, relationships to MLPs and kernel methods, and applications.
Influence-guided data selection for RLVR with theoretical guarantees for improving LLM reasoning efficiency.
In-context policy optimization for large reasoning models using off-policy exploration to improve RLVR reasoning capabilities.
Hamiltonian Gaussian Processes for learning physically consistent dynamics from input-output data without velocity information.
Analysis of barren plateau problem in quantum denoising diffusion models for learning quantum and classical data.
Transfer learning via classifier guidance for discrete diffusion models in small-data regimes, extending continuous diffusion techniques.
Vector quantization technique for optimizing Kolmogorov-Arnold Network inference on edge devices with memory constraints.
Neural network approach for estimating scattered radiation fields in interventional radiology with synthetic training datasets.
Review of diffusion models for simulation-based inference with intractable likelihoods, covering theoretical foundations and applications.
ML model predicting time pressure effects on motorcycle riders using 129k+ time-series sequences for safety interventions.
Computationally efficient algorithms for swap regret minimization in online optimization with connections to correlated equilibrium and non-manipulability.