{"total":165,"rows":[{"id":"2103.14749","title":"Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks","created":"2021-03-26","cited_by_count":395,"area":null},{"id":"2504.20571","title":"Reinforcement Learning for Reasoning in Large Language Models with One Training Example","created":"2025-04-29","cited_by_count":295,"area":"lr"},{"id":"2302.10149","title":"Poisoning Web-Scale Training Datasets is Practical","created":"2023-02-20","cited_by_count":290,"area":null},{"id":"2206.07137","title":"Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt","created":"2022-06-14","cited_by_count":236,"area":null},{"id":"2104.01633","title":"MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection","created":"2021-04-04","cited_by_count":138,"area":null},{"id":"2210.15809","title":"Coverage-centric Coreset Selection for High Pruning Rates","created":"2022-10-28","cited_by_count":117,"area":null},{"id":"2204.04879","title":"How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision","created":"2022-04-11","cited_by_count":102,"area":null},{"id":"2505.22660","title":"Maximizing Confidence Alone Improves Reasoning","created":"2025-05-28","cited_by_count":100,"area":"lr"},{"id":"2106.09708","title":"Multi-Label Learning from Single Positive Labels","created":"2021-06-17","cited_by_count":81,"area":null},{"id":"2105.14602","title":"On the geometry of generalization and memorization in deep neural networks","created":"2021-05-30","cited_by_count":73,"area":null},{"id":"2111.00418","title":"Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances","created":"2021-10-31","cited_by_count":54,"area":null},{"id":"2210.07179","title":"MAPL: Parameter-Efficient Adaptation of Unimodal Pre-Trained Models for Vision-Language Few-Shot Prompting","created":"2022-10-13","cited_by_count":41,"area":null},{"id":"2102.02515","title":"HYDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks","created":"2021-02-04","cited_by_count":38,"area":null},{"id":"2106.09291","title":"Towards Understanding Deep Learning from Noisy Labels with Small-Loss Criterion","created":"2021-06-17","cited_by_count":36,"area":null},{"id":"2410.21869","title":"Cross-Entropy Is All You Need To Invert the Data Generating Process","created":"2024-10-29","cited_by_count":31,"area":null},{"id":"2502.01612","title":"Self-Improving Transformers Overcome Easy-to-Hard and Length Generalization Challenges","created":"2025-02-03","cited_by_count":31,"area":"synth"},{"id":"2110.09022","title":"Mitigating Memorization of Noisy Labels via Regularization between Representations","created":"2021-10-18","cited_by_count":26,"area":null},{"id":"2203.10036","title":"On the Generalization Mystery in Deep Learning","created":"2022-03-18","cited_by_count":25,"area":null},{"id":"2204.09092","title":"Indiscriminate Data Poisoning Attacks on Neural Networks","created":"2022-04-19","cited_by_count":25,"area":"safety"},{"id":"2102.06315","title":"Segmentation-Renormalized Deep Feature Modulation for Unpaired Image Harmonization","created":"2021-02-11","cited_by_count":24,"area":null},{"id":"2102.12191","title":"DeepCervix: A Deep Learning-based Framework for the Classification of Cervical Cells Using Hybrid Deep Feature Fusion Techniques","created":"2021-02-24","cited_by_count":23,"area":null},{"id":"2405.03676","title":"Why is SAM Robust to Label Noise?","created":"2024-05-06","cited_by_count":21,"area":null},{"id":"2409.09603","title":"Towards Data-Centric RLHF: Simple Metrics for Preference Dataset Comparison","created":"2024-09-15","cited_by_count":21,"area":"alignment"},{"id":"2105.00303","title":"RATT: Leveraging Unlabeled Data to Guarantee Generalization","created":"2021-05-01","cited_by_count":20,"area":null},{"id":"2202.08141","title":"FUN-SIS: a Fully UNsupervised approach for Surgical Instrument Segmentation","created":"2022-02-16","cited_by_count":20,"area":null},{"id":"2207.13378","title":"Identifying Hard Noise in Long-Tailed Sample Distribution","created":"2022-07-27","cited_by_count":19,"area":null},{"id":"2302.13959","title":"Make Every Example Count: On the Stability and Utility of Self-Influence for Learning from Noisy NLP Datasets","created":"2023-02-27","cited_by_count":19,"area":null},{"id":"2303.13174","title":"3D-POP -- An automated annotation approach to facilitate markerless 2D-3D tracking of freely moving birds with marker-based motion capture","created":"2023-03-23","cited_by_count":18,"area":null},{"id":"2101.08732","title":"Self-Adaptive Training: Bridging Supervised and Self-Supervised Learning","created":"2021-01-21","cited_by_count":17,"area":null},{"id":"2208.08464","title":"CTRL: Clustering Training Losses for Label Error Detection","created":"2022-08-17","cited_by_count":17,"area":null},{"id":"2304.10655","title":"The Dataset Multiplicity Problem: How Unreliable Data Impacts Predictions","created":"2023-04-20","cited_by_count":17,"area":null},{"id":"2308.13862","title":"Late Stopping: Avoiding Confidently Learning from Mislabeled Examples","created":"2023-08-26","cited_by_count":17,"area":null},{"id":"2104.01493","title":"Exponentiated Gradient Reweighting for Robust Training Under Label Noise and Beyond","created":"2021-04-03","cited_by_count":16,"area":null},{"id":"2205.12702","title":"Detecting Label Errors by using Pre-Trained Language Models","created":"2022-05-25","cited_by_count":16,"area":null},{"id":"2112.08796","title":"Saliency Grafting: Innocuous Attribution-Guided Mixup with Calibrated Label Mixing","created":"2021-12-16","cited_by_count":15,"area":null},{"id":"2206.01106","title":"Robustness to Label Noise Depends on the Shape of the Noise Distribution in Feature Space","created":"2022-06-02","cited_by_count":15,"area":null},{"id":"2107.11413","title":"An Instance-Dependent Simulation Framework for Learning with Label Noise","created":"2021-07-23","cited_by_count":14,"area":null},{"id":"2103.11395","title":"ScanMix: Learning from Severe Label Noise via Semantic Clustering and Semi-Supervised Learning","created":"2021-03-21","cited_by_count":13,"area":null},{"id":"2205.15367","title":"Non-Markovian Reward Modelling from Trajectory Labels via Interpretable Multiple Instance Learning","created":"2022-05-30","cited_by_count":13,"area":"alignment"},{"id":"2402.13635","title":"The METRIC-framework for assessing data quality for trustworthy AI in medicine: a systematic review","created":"2024-02-21","cited_by_count":13,"area":null},{"id":"2404.01775","title":"A noisy elephant in the room: Is your out-of-distribution detector robust to label noise?","created":"2024-04-02","cited_by_count":13,"area":null},{"id":"2102.03049","title":"Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database-HF_Lung_V1","created":"2021-02-05","cited_by_count":12,"area":null},{"id":"2105.09198","title":"A Privacy-Preserving Approach to Extraction of Personal Information through Automatic Annotation and Federated Learning","created":"2021-05-19","cited_by_count":12,"area":null},{"id":"2210.14019","title":"The Curious Case of Benign Memorization","created":"2022-10-25","cited_by_count":12,"area":null},{"id":"2303.09977","title":"Semantic Scene Completion with Cleaner Self","created":"2023-03-17","cited_by_count":12,"area":null},{"id":"2105.00349","title":"Estimating the electrical power output of industrial devices with end-to-end time-series classification in the presence of label noise","created":"2021-05-01","cited_by_count":10,"area":null},{"id":"2203.03443","title":"Generalization Through The Lens Of Leave-One-Out Error","created":"2022-03-07","cited_by_count":10,"area":null},{"id":"2302.07560","title":"Unsupervised classification to improve the quality of a bird song recording dataset","created":"2023-02-15","cited_by_count":10,"area":null},{"id":"2303.01806","title":"When does Privileged Information Explain Away Label Noise?","created":"2023-03-03","cited_by_count":10,"area":null},{"id":"2112.15367","title":"Weakly Supervised Change Detection Using Guided Anisotropic Difusion","created":"2021-12-31","cited_by_count":8,"area":null},{"id":"2210.04525","title":"SelfMix: Robust Learning Against Textual Label Noise with Self-Mixup Training","created":"2022-10-10","cited_by_count":8,"area":null},{"id":"2305.17048","title":"Intrinsic Self-Supervision for Data Quality Audits","created":"2023-05-26","cited_by_count":8,"area":null},{"id":"2107.09546","title":"Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions","created":"2021-07-20","cited_by_count":7,"area":null},{"id":"2301.05154","title":"Mephisto: A Framework for Portable, Reproducible, and Iterative Crowdsourcing","created":"2023-01-12","cited_by_count":7,"area":null},{"id":"2306.06804","title":"Neural Machine Translation for the Indigenous Languages of the Americas: An Introduction","created":"2023-06-11","cited_by_count":7,"area":null},{"id":"2310.02533","title":"Quantifying and mitigating the impact of label errors on model disparity metrics","created":"2023-10-04","cited_by_count":7,"area":null},{"id":"2401.04305","title":"Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitions","created":"2024-01-09","cited_by_count":7,"area":null},{"id":"2505.18434","title":"TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP","created":"2025-05-24","cited_by_count":7,"area":"mllm"},{"id":"2104.05261","title":"Robust Classification from Noisy Labels: Integrating Additional Knowledge for Chest Radiography Abnormality Assessment","created":"2021-04-12","cited_by_count":6,"area":null},{"id":"2108.00114","title":"On The State of Data In Computer Vision: Human Annotations Remain Indispensable for Developing Deep Learning Models","created":"2021-07-31","cited_by_count":6,"area":null},{"id":"2201.03425","title":"Towards Trustworthy AutoGrading of Short, Multi-lingual, Multi-type Answers","created":"2022-01-02","cited_by_count":6,"area":null},{"id":"2302.08180","title":"Cross Modal Distillation for Flood Extent Mapping","created":"2023-02-16","cited_by_count":6,"area":"distill"},{"id":"2309.06961","title":"Towards Reliable Dermatology Evaluation Benchmarks","created":"2023-09-13","cited_by_count":6,"area":null},{"id":"2403.01942","title":"Mitigating Label Noise on Graph via Topological Sample Selection","created":"2024-03-04","cited_by_count":6,"area":null},{"id":"2205.01184","title":"Performance Weighting for Robust Federated Learning Against Corrupted Sources","created":"2022-05-02","cited_by_count":5,"area":null},{"id":"2302.12304","title":"Uncertainty Injection: A Deep Learning Method for Robust Optimization","created":"2023-02-23","cited_by_count":5,"area":null},{"id":"2501.07886","title":"Iterative Label Refinement Matters More than Preference Optimization under Weak Supervision","created":"2025-01-14","cited_by_count":5,"area":"alignment"},{"id":"2507.04782","title":"Reason to Rote: Rethinking Memorization in Reasoning","created":"2025-07-07","cited_by_count":5,"area":"lr"},{"id":"2103.05617","title":"Point-supervised Segmentation of Microscopy Images and Volumes via Objectness Regularization","created":"2021-03-09","cited_by_count":4,"area":null},{"id":"2205.15895","title":"From Keypoints to Object Landmarks via Self-Training Correspondence: A novel approach to Unsupervised Landmark Discovery","created":"2022-05-31","cited_by_count":4,"area":null},{"id":"2305.08826","title":"Learning Better Contrastive View from Radiologist's Gaze","created":"2023-05-15","cited_by_count":4,"area":null},{"id":"2307.10718","title":"Differences Between Hard and Noisy-labeled Samples: An Empirical Study","created":"2023-07-20","cited_by_count":4,"area":null},{"id":"2401.10337","title":"Noise Contrastive Estimation-based Matching Framework for Low-Resource Security Attack Pattern Recognition","created":"2024-01-18","cited_by_count":4,"area":null},{"id":"2412.18296","title":"Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies","created":"2024-12-24","cited_by_count":4,"area":null},{"id":"2507.18183","title":"ChronoSelect: Robust Learning with Noisy Labels via Dynamics Temporal Memory","created":"2025-07-24","cited_by_count":4,"area":null},{"id":"2101.05339","title":"Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties","created":"2021-01-13","cited_by_count":3,"area":null},{"id":"2106.07353","title":"Posthoc Verification and the Fallibility of the Ground Truth","created":"2021-06-02","cited_by_count":3,"area":null},{"id":"2107.04497","title":"Batch Inverse-Variance Weighting: Deep Heteroscedastic Regression","created":"2021-07-09","cited_by_count":3,"area":null},{"id":"2111.03056","title":"Bootstrap Your Object Detector via Mixed Training","created":"2021-11-04","cited_by_count":3,"area":null},{"id":"2112.00350","title":"Investigation of Training Label Error Impact on RNN-T","created":"2021-12-01","cited_by_count":3,"area":null},{"id":"2206.03575","title":"Certifying Data-Bias Robustness in Linear Regression","created":"2022-06-07","cited_by_count":3,"area":null},{"id":"2310.10543","title":"ViPE: Visualise Pretty-much Everything","created":"2023-10-16","cited_by_count":3,"area":null},{"id":"2312.02200","title":"An Empirical Study of Automated Mislabel Detection in Real World Vision Datasets","created":"2023-12-02","cited_by_count":3,"area":null},{"id":"2408.14348","title":"Deep learning-based ecological analysis of camera trap images is impacted by training data quality and quantity","created":"2024-08-26","cited_by_count":3,"area":null},{"id":"2409.06716","title":"Detailed delineation of the fetal brain in diffusion MRI via multi-task learning","created":"2024-08-26","cited_by_count":3,"area":null},{"id":"2503.19886","title":"RCC-PFL: Robust Client Clustering under Noisy Labels in Personalized Federated Learning","created":"2025-03-25","cited_by_count":3,"area":null},{"id":"2505.08662","title":"Revealing economic facts: LLMs know more than they say","created":"2025-05-13","cited_by_count":3,"area":null},{"id":"2105.04806","title":"Deep scattering network for speech emotion recognition","created":"2021-05-11","cited_by_count":2,"area":null},{"id":"2110.06827","title":"NoisyActions2M: A Multimedia Dataset for Video Understanding from Noisy Labels","created":"2021-10-13","cited_by_count":2,"area":null},{"id":"2202.03674","title":"Trained Model in Supervised Deep Learning is a Conditional Risk Minimizer","created":"2022-02-08","cited_by_count":2,"area":null},{"id":"2207.12546","title":"The Bearable Lightness of Big Data: Towards Massive Public Datasets in Scientific Machine Learning","created":"2022-07-25","cited_by_count":2,"area":null},{"id":"2211.06112","title":"Towards automating Numerical Consistency Checks in Financial Reports","created":"2022-11-11","cited_by_count":2,"area":null},{"id":"2301.13631","title":"TopoBERT: Plug and Play Toponym Recognition Module Harnessing Fine-tuned BERT","created":"2023-01-31","cited_by_count":2,"area":null},{"id":"2407.10992","title":"AlleNoise: large-scale text classification benchmark dataset with real-world label noise","created":"2024-06-24","cited_by_count":2,"area":null},{"id":"2410.14612","title":"MultiOrg: A Multi-rater Organoid-detection Dataset","created":"2024-10-18","cited_by_count":2,"area":null},{"id":"2411.02935","title":"A High Resolution Urban and Rural Settlement Map of Africa Using Deep Learning and Satellite Imagery","created":"2024-11-05","cited_by_count":2,"area":null},{"id":"2506.16924","title":"Real-Time Black-Box Optimization for Dynamic Discrete Environments Using Embedded Ising Machines","created":"2025-06-20","cited_by_count":2,"area":null},{"id":"2512.11912","title":"Robustness of Probabilistic Models to Low-Quality Data: A Multi-Perspective Analysis","created":"2025-12-11","cited_by_count":2,"area":null},{"id":"2602.02400","title":"An Empirical Study on Noisy Data and LLM Pretraining Loss Divergence","created":"2026-02-02","cited_by_count":2,"area":null},{"id":"2603.06467","title":"GreenRFM: Learning a resource-efficient radiology vision-language foundation model via supervision-centric pre-training","created":"2026-03-06","cited_by_count":2,"area":"mllm"}],"next_cursor":"100"}