9503 papers
This paper establishes nearly optimal super-approximation error bounds of order and in and norms, respectively, for ReLU neural networks approximating Korobov functions by leveraging sparse grid finite elements and bit extraction, thereby demonstrating that neural network expressivity effectively overcomes the curse of dimensionality.
This paper proposes a kernel-based maximum causal entropy inverse reinforcement learning framework for infinite-horizon stationary mean-field games that models unknown rewards in a reproducing kernel Hilbert space to capture nonlinear structures, proves the algorithm's theoretical consistency via Fréchet differentiability, and demonstrates superior policy recovery performance over linear baselines in traffic routing scenarios while extending the approach to finite-horizon non-stationary settings.
This paper proposes EDA, a unified theoretical framework that extends diffusion models to handle arbitrary noise patterns without increasing computational overhead, thereby significantly improving performance and generalization in diverse image restoration tasks such as medical imaging and natural scene recovery.
This paper proposes a quantum version of the EM algorithm for training semi-quantum restricted Boltzmann machines, demonstrating that this information-geometric approach effectively circumvents the barren plateau problem and outperforms gradient-based methods in quantum generative modeling.
This paper introduces TIC-GRPO, a provably convergent and more efficient variant of the critic-free GRPO algorithm that replaces token-level importance sampling with trajectory-level correction to better estimate current policy gradients, demonstrating superior performance on math and coding tasks.
This study proposes a rigorous evaluation framework for automated neonatal seizure detection that addresses current metric inconsistencies by recommending balanced metrics, comprehensive sensitivity/specificity reporting, and multi-rater Turing tests to ensure reliable, expert-level validation for clinical adoption.
This paper presents the first systematic study of in-training safeguards against emergent misalignment in fine-tuned language models, demonstrating that interleaving training examples selected by the perplexity gap between aligned and misaligned models effectively prevents broad misalignment while preserving task performance and coherence.
This paper introduces a fast method to evaluate the robustness of LLM rankings, revealing that top model positions in crowdsourced platforms like Chatbot Arena are surprisingly sensitive to the removal of a tiny fraction of preference data, whereas rankings from expert-annotated benchmarks like MT-bench remain more stable.
This study comprehensively evaluates how weight and activation quantization influences various forms of bias in large language models, revealing that while it can reduce toxicity and preserve sentiment, it often exacerbates stereotypes and unfairness in generative tasks, particularly under aggressive compression.
This paper presents a comprehensive survey of Multi-Agent Reinforcement Learning applications in Intelligent Transportation Systems, offering a structured taxonomy of algorithms and domains, reviewing key simulation platforms, and identifying critical challenges hindering real-world deployment.
This paper attributes the performance degradation of GNN-based SAT solvers on difficult instances to inherent negative graph Ricci curvature in formula representations, which causes oversquashing by creating local connectivity bottlenecks that hinder the compression of long-range dependencies.
This paper proposes an unbalanced optimal transport-based alignment model that reframes acoustic-linguistic matching as a detection problem to effectively handle structural asymmetries and distributional mismatches, thereby improving knowledge transfer performance in CTC-based automatic speech recognition systems.
This paper introduces AttnBoost, an interpretable framework that integrates feature-level attention into gradient boosting to dynamically prioritize relevant variables like promotions and seasonality, thereby improving both sales forecasting accuracy and actionable insights for retail supply chain management.
This paper proposes a reservoir structure optimization method based on persistent GLMY homology theory, demonstrating that modifying minimal representative cycles of one-dimensional homology groups enhances reservoir performance in relation to dataset periodicity.
This paper introduces TabStruct, a comprehensive evaluation benchmark and a novel "global utility" metric that jointly assesses the structural fidelity and conventional performance of tabular data generators across 29 real-world datasets without requiring ground-truth causal structures.
The paper introduces BabyHuBERT, a multilingual self-supervised speech model trained on 13,000 hours of child-centered recordings that significantly outperforms existing adult-focused models in segmenting speakers within diverse, naturalistic child language datasets.
This paper introduces Diffusion-Based Impedance Learning, a framework that combines a Transformer-based diffusion model with energy-consistent impedance control to enable robots to learn and adapt contact-rich manipulation behaviors from teleoperated demonstrations, achieving high-precision performance and robust generalization in tasks like peg-in-hole insertion.
This paper introduces Complexity-Regularized Proximal Policy Optimization (CR-PPO), a novel algorithm that replaces standard entropy regularization with a self-regulating complexity term—defined as the product of Shannon entropy and disequilibrium—to maintain beneficial stochasticity while reducing sensitivity to hyperparameter tuning and avoiding the overriding of reward signals.
This paper introduces Noise-to-Notes (N2N), a state-of-the-art diffusion-based framework that redefines automatic drum transcription as a conditional generative task, utilizing an Annealed Pseudo-Huber loss for joint optimization and music foundation model features to achieve superior robustness and performance across multiple benchmarks.
BridgeDrive introduces a novel anchor-guided diffusion bridge policy that ensures theoretical consistency between forward and reverse processes to transform coarse expert trajectories into refined, safe, and reactive closed-loop plans, achieving state-of-the-art performance on the Bench2Drive benchmark.