{"total":16,"rows":[{"id":"2509.03036","title":"Knowledge Integration for Physics-informed Symbolic Regression Using Pre-trained Large Language Models","created":"2025-09-03","cited_by_count":7,"area":null},{"id":"2505.00812","title":"Handling Label Noise via Instance-Level Difficulty Modeling and Dynamic Optimization","created":"2025-05-01","cited_by_count":6,"area":null},{"id":"2502.08227","title":"Enhancing Sample Selection Against Label Noise by Cutting Mislabeled Easy Examples","created":"2025-02-12","cited_by_count":4,"area":null},{"id":"2504.06805","title":"Robust Classification with Noisy Labels Based on Posterior Maximization","created":"2025-04-09","cited_by_count":2,"area":null},{"id":"2505.12952","title":"LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data","created":"2025-05-19","cited_by_count":1,"area":null},{"id":"2510.16074","title":"Early-stopping for Transformer model training","created":"2025-10-17","cited_by_count":1,"area":null},{"id":"2510.25480","title":"Gradient-Weight Alignment as a Train-Time Proxy for Generalization in Classification Tasks","created":"2025-10-29","cited_by_count":1,"area":null},{"id":"2609.04009","title":"The Blind Spot in 2D Infants' Pose Estimation:Robust Learning from Noisy Annotations","created":"2026-09-03","cited_by_count":1,"area":null},{"id":"2504.09085","title":"crowd-hpo: Realistic Hyperparameter Optimization and Benchmarking for Learning from Crowds with Noisy Labels","created":"2025-04-12","cited_by_count":0,"area":null},{"id":"2505.11006","title":"Is Supervised Learning Really That Different from Unsupervised?","created":"2025-05-16","cited_by_count":0,"area":null},{"id":"2507.05309","title":"Neural Velocity for hyperparameter tuning","created":"2025-07-07","cited_by_count":0,"area":null},{"id":"2507.11807","title":"CLID-MU: Cross-Layer Information Divergence Based Meta Update Strategy for Learning with Noisy Labels","created":"2025-07-16","cited_by_count":0,"area":null},{"id":"2508.19028","title":"GRADSTOP: Early Stopping of Gradient Descent via Posterior Sampling","created":"2025-08-26","cited_by_count":0,"area":null},{"id":"2509.01842","title":"GradES: Significantly Faster Training in Transformers with Gradient-Based Early Stopping","created":"2025-09-01","cited_by_count":0,"area":null},{"id":"2605.31090","title":"On Revisiting Entropy for Identifying Mislabeled Images","created":"2026-05-29","cited_by_count":0,"area":null},{"id":"2609.21017","title":"A Smoothed Discrepancy Principle for Random Feature Methods and Neural Networks","created":"2026-09-17","cited_by_count":0,"area":null}],"next_cursor":null}