Cs-Lg
An interpretable data-driven approach to optimizing clinical fall risk assessment
Optimizing Mirror-Image Peptide Sequence Design for Data Storage via Peptide Bond Cleavage Prediction
Matcha: Multi-Stage Riemannian Flow Matching for Accurate and Physically Valid Molecular Docking
DoRAN: Stabilizing Weight-Decomposed Low-Rank Adaptation via Noise Injection and Auxiliary Networks
Teaching Models to Teach Themselves: Reasoning at the Edge of Learnability
FuSeFL: Fully Secure and Scalable Federated Learning
A Unified Framework for Lifted Training and Inversion Approaches
Discriminative Feature Feedback with General Teacher Classes
Inverse problems with diffusion models: MAP estimation via mode-seeking loss
Learning Nonlinear Heterogeneity in Physical Kolmogorov-Arnold Networks
Think-Augmented Function Calling: Improving LLM Parameter Accuracy Through Embedded Reasoning
To Grok Grokking: Provable Grokking in Ridge Regression
Anomaly Resilient Temporal QoS Prediction using Hypergraph Convoluted Transformer Network
Data-Centric Interpretability for LLM-based Multi-Agent Reinforcement Learning
CORP: Closed-Form One-shot Representation-Preserving Structured Pruning for Vision Transformers
