{"total":464,"rows":[{"id":"2103.10360","title":"GLM: General Language Model Pretraining with Autoregressive Blank Infilling","created":"2021-03-18","cited_by_count":1031,"area":null},{"id":"2107.03342","title":"A Survey of Uncertainty in Deep Neural Networks","created":"2021-07-07","cited_by_count":680,"area":null},{"id":"2212.13345","title":"The Forward-Forward Algorithm: Some Preliminary Investigations","created":"2022-12-27","cited_by_count":266,"area":null},{"id":"2311.08298","title":"A Survey of Confidence Estimation and Calibration in Large Language Models","created":"2023-11-14","cited_by_count":256,"area":null},{"id":"2303.07109","title":"Transformer-based World Models Are Happy With 100k Interactions","created":"2023-03-13","cited_by_count":139,"area":"wm"},{"id":"2102.00650","title":"Rethinking Soft Labels for Knowledge Distillation: A Bias-Variance Tradeoff Perspective","created":"2021-02-01","cited_by_count":119,"area":"distill"},{"id":"2202.00666","title":"Locally Typical Sampling","created":"2022-02-01","cited_by_count":111,"area":null},{"id":"2208.14933","title":"Membership Inference Attacks by Exploiting Loss Trajectory","created":"2022-08-31","cited_by_count":101,"area":"safety"},{"id":"2111.15430","title":"The Devil is in the Margin: Margin-based Label Smoothing for Network Calibration","created":"2021-11-30","cited_by_count":91,"area":null},{"id":"2103.02130","title":"Augmentation Strategies for Learning with Noisy Labels","created":"2021-03-03","cited_by_count":86,"area":null},{"id":"2106.06333","title":"Invariant Information Bottleneck for Domain Generalization","created":"2021-06-11","cited_by_count":86,"area":null},{"id":"2103.13258","title":"Dynamic Slimmable Network","created":"2021-03-24","cited_by_count":83,"area":null},{"id":"2103.15916","title":"Robust Audio-Visual Instance Discrimination","created":"2021-03-29","cited_by_count":80,"area":null},{"id":"2109.05211","title":"RobustART: Benchmarking Robustness on Architecture Design and Training Techniques","created":"2021-09-11","cited_by_count":79,"area":null},{"id":"2308.01222","title":"Calibration in Deep Learning: A Survey of the State-of-the-Art","created":"2023-08-02","cited_by_count":75,"area":null},{"id":"2106.04149","title":"To Smooth or Not? When Label Smoothing Meets Noisy Labels","created":"2021-06-08","cited_by_count":70,"area":null},{"id":"2408.16673","title":"Preserving Diversity in Supervised Fine-Tuning of Large Language Models","created":"2024-08-29","cited_by_count":70,"area":null},{"id":"2401.05778","title":"Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems","created":"2024-01-11","cited_by_count":66,"area":null},{"id":"2204.12511","title":"PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions","created":"2022-04-26","cited_by_count":63,"area":null},{"id":"2203.14675","title":"Part-based Pseudo Label Refinement for Unsupervised Person Re-identification","created":"2022-03-28","cited_by_count":59,"area":null},{"id":"2303.02045","title":"Uncertainty Estimation by Fisher Information-based Evidential Deep Learning","created":"2023-03-03","cited_by_count":58,"area":null},{"id":"2211.00151","title":"A Close Look into the Calibration of Pre-trained Language Models","created":"2022-10-31","cited_by_count":57,"area":null},{"id":"2204.04588","title":"Robust Cross-Modal Representation Learning with Progressive Self-Distillation","created":"2022-04-10","cited_by_count":55,"area":"distill"},{"id":"2104.00676","title":"Is Label Smoothing Truly Incompatible with Knowledge Distillation: An Empirical Study","created":"2021-04-01","cited_by_count":51,"area":"distill"},{"id":"2310.17918","title":"Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method","created":"2023-10-27","cited_by_count":51,"area":null},{"id":"2110.11027","title":"FedGEMS: Federated Learning of Larger Server Models via Selective Knowledge Fusion","created":"2021-10-21","cited_by_count":49,"area":null},{"id":"2111.09277","title":"SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness","created":"2021-11-17","cited_by_count":49,"area":null},{"id":"2203.13834","title":"A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration","created":"2022-03-25","cited_by_count":49,"area":null},{"id":"2110.14222","title":"Sample Selection for Fair and Robust Training","created":"2021-10-27","cited_by_count":48,"area":null},{"id":"2210.07810","title":"A Consistent and Differentiable Lp Canonical Calibration Error Estimator","created":"2022-10-13","cited_by_count":47,"area":null},{"id":"2104.06951","title":"Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey","created":"2021-04-14","cited_by_count":46,"area":null},{"id":"2301.08915","title":"Improving Deep Regression with Ordinal Entropy","created":"2023-01-21","cited_by_count":45,"area":null},{"id":"2101.03453","title":"BERT & Family Eat Word Salad: Experiments with Text Understanding","created":"2021-01-10","cited_by_count":44,"area":null},{"id":"2109.00574","title":"Active label cleaning for improved dataset quality under resource constraints","created":"2021-09-01","cited_by_count":44,"area":null},{"id":"2310.19208","title":"LitCab: Lightweight Language Model Calibration over Short- and Long-form Responses","created":"2023-10-30","cited_by_count":44,"area":null},{"id":"2311.07052","title":"Towards the Law of Capacity Gap in Distilling Language Models","created":"2023-11-13","cited_by_count":44,"area":"distill"},{"id":"2109.14449","title":"One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective","created":"2021-09-29","cited_by_count":43,"area":null},{"id":"2207.00810","title":"Eliciting and Learning with Soft Labels from Every Annotator","created":"2022-07-02","cited_by_count":43,"area":null},{"id":"2303.02970","title":"Rethinking Confidence Calibration for Failure Prediction","created":"2023-03-06","cited_by_count":43,"area":null},{"id":"2201.08025","title":"Low-Pass Filtering SGD for Recovering Flat Optima in the Deep Learning Optimization Landscape","created":"2022-01-20","cited_by_count":39,"area":null},{"id":"2403.08819","title":"Thermometer: Towards Universal Calibration for Large Language Models","created":"2024-02-20","cited_by_count":38,"area":null},{"id":"2207.05801","title":"RelaxLoss: Defending Membership Inference Attacks without Losing Utility","created":"2022-07-12","cited_by_count":37,"area":"safety"},{"id":"2402.11148","title":"Knowledge Distillation Based on Transformed Teacher Matching","created":"2024-02-17","cited_by_count":37,"area":"distill"},{"id":"2104.14235","title":"Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety","created":"2021-04-29","cited_by_count":35,"area":null},{"id":"2105.04290","title":"Meta-Cal: Well-controlled Post-hoc Calibration by Ranking","created":"2021-05-10","cited_by_count":33,"area":null},{"id":"2109.06270","title":"STraTA: Self-Training with Task Augmentation for Better Few-shot Learning","created":"2021-09-13","cited_by_count":33,"area":"synth"},{"id":"2208.11351","title":"Self-Filtering: A Noise-Aware Sample Selection for Label Noise with Confidence Penalization","created":"2022-08-24","cited_by_count":33,"area":null},{"id":"2210.11464","title":"Self-Supervised Learning via Maximum Entropy Coding","created":"2022-10-20","cited_by_count":33,"area":null},{"id":"2103.06132","title":"MixMo: Mixing Multiple Inputs for Multiple Outputs via Deep Subnetworks","created":"2021-03-10","cited_by_count":32,"area":null},{"id":"2206.14532","title":"Revisiting Label Smoothing and Knowledge Distillation Compatibility: What was Missing?","created":"2022-06-29","cited_by_count":32,"area":"distill"},{"id":"2303.05506","title":"TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization","created":"2023-03-09","cited_by_count":32,"area":null},{"id":"2104.05788","title":"Spatially Varying Label Smoothing: Capturing Uncertainty from Expert Annotations","created":"2021-04-12","cited_by_count":31,"area":null},{"id":"2212.10888","title":"A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability","created":"2022-12-21","cited_by_count":31,"area":null},{"id":"2103.13811","title":"Student Network Learning via Evolutionary Knowledge Distillation","created":"2021-03-23","cited_by_count":30,"area":"distill"},{"id":"2102.03349","title":"On the Reproducibility of Neural Network Predictions","created":"2021-02-05","cited_by_count":29,"area":null},{"id":"2112.07431","title":"Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation","created":"2021-12-14","cited_by_count":29,"area":null},{"id":"2301.02828","title":"Why do Nearest Neighbor Language Models Work?","created":"2023-01-07","cited_by_count":29,"area":"rag"},{"id":"2407.13588","title":"Robust Calibration of Large Vision-Language Adapters","created":"2024-07-18","cited_by_count":28,"area":null},{"id":"2202.13587","title":"Rethinking and Refining the Distinct Metric","created":"2022-02-28","cited_by_count":27,"area":null},{"id":"2209.07601","title":"Towards Improving Calibration in Object Detection Under Domain Shift","created":"2022-09-15","cited_by_count":26,"area":null},{"id":"2105.14556","title":"Diversifying Dialog Generation via Adaptive Label Smoothing","created":"2021-05-30","cited_by_count":25,"area":null},{"id":"2203.16219","title":"Acknowledging the Unknown for Multi-label Learning with Single Positive Labels","created":"2022-03-30","cited_by_count":24,"area":null},{"id":"2208.00461","title":"Adaptive Temperature Scaling for Robust Calibration of Deep Neural Networks","created":"2022-07-31","cited_by_count":24,"area":null},{"id":"2310.04752","title":"A Unified Perspective for Loss-Oriented Imbalanced Learning via Localization","created":"2023-10-07","cited_by_count":24,"area":null},{"id":"2103.16940","title":"Learning with Memory-based Virtual Classes for Deep Metric Learning","created":"2021-03-31","cited_by_count":21,"area":null},{"id":"2211.14058","title":"Cross-Domain Ensemble Distillation for Domain Generalization","created":"2022-11-25","cited_by_count":21,"area":null},{"id":"2305.06024","title":"A Survey on the Robustness of Computer Vision Models against Common Corruptions","created":"2023-05-10","cited_by_count":21,"area":null},{"id":"2307.11978","title":"Why Is Prompt Tuning for Vision-Language Models Robust to Noisy Labels?","created":"2023-07-22","cited_by_count":21,"area":"mllm"},{"id":"2103.03451","title":"Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels","created":"2021-03-05","cited_by_count":20,"area":null},{"id":"2110.07586","title":"Can Explanations Be Useful for Calibrating Black Box Models?","created":"2021-10-14","cited_by_count":20,"area":null},{"id":"2204.02620","title":"Towards Robust Adaptive Object Detection under Noisy Annotations","created":"2022-04-06","cited_by_count":20,"area":null},{"id":"2212.07086","title":"NLIP: Noise-robust Language-Image Pre-training","created":"2022-12-14","cited_by_count":20,"area":null},{"id":"2312.00600","title":"Improving Plasticity in Online Continual Learning via Collaborative Learning","created":"2023-12-01","cited_by_count":20,"area":null},{"id":"2101.10160","title":"Measuring Dependence with Matrix-based Entropy Functional","created":"2021-01-25","cited_by_count":19,"area":null},{"id":"2210.12265","title":"On the Calibration of Massively Multilingual Language Models","created":"2022-10-21","cited_by_count":19,"area":null},{"id":"2308.11990","title":"RankMixup: Ranking-Based Mixup Training for Network Calibration","created":"2023-08-23","cited_by_count":19,"area":null},{"id":"2102.00533","title":"Deep Deterministic Information Bottleneck with Matrix-based Entropy Functional","created":"2021-01-31","cited_by_count":18,"area":null},{"id":"2103.05152","title":"Knowledge Evolution in Neural Networks","created":"2021-03-09","cited_by_count":18,"area":null},{"id":"2104.07275","title":"Span Pointer Networks for Non-Autoregressive Task-Oriented Semantic Parsing","created":"2021-04-15","cited_by_count":18,"area":null},{"id":"2112.02960","title":"Two Wrongs Don't Make a Right: Combating Confirmation Bias in Learning with Label Noise","created":"2021-12-06","cited_by_count":18,"area":null},{"id":"2212.10005","title":"Calibrating Deep Neural Networks using Explicit Regularisation and Dynamic Data Pruning","created":"2022-12-20","cited_by_count":18,"area":null},{"id":"2304.03981","title":"Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification","created":"2023-04-08","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":"2103.15454","title":"Proxy Synthesis: Learning with Synthetic Classes for Deep Metric Learning","created":"2021-03-29","cited_by_count":17,"area":null},{"id":"2104.04923","title":"Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog","created":"2021-04-11","cited_by_count":17,"area":null},{"id":"2208.09833","title":"Label-Noise Learning with Intrinsically Long-Tailed Data","created":"2022-08-21","cited_by_count":17,"area":null},{"id":"2305.18377","title":"BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning","created":"2023-05-28","cited_by_count":16,"area":null},{"id":"2306.08271","title":"Multiclass Confidence and Localization Calibration for Object Detection","created":"2023-06-14","cited_by_count":16,"area":null},{"id":"2211.13723","title":"Beyond Losses Reweighting: Empowering Multi-Task Learning via the Generalization Perspective","created":"2022-11-24","cited_by_count":15,"area":null},{"id":"2303.02404","title":"Fine-Grained Classification with Noisy Labels","created":"2023-03-04","cited_by_count":15,"area":null},{"id":"2304.10127","title":"Learning Sample Difficulty from Pre-trained Models for Reliable Prediction","created":"2023-04-20","cited_by_count":15,"area":null},{"id":"2308.11911","title":"ACLS: Adaptive and Conditional Label Smoothing for Network Calibration","created":"2023-08-23","cited_by_count":15,"area":null},{"id":"2409.09582","title":"NEVLP: Noise-Robust Framework for Efficient Vision-Language Pre-training","created":"2024-09-15","cited_by_count":15,"area":"mllm"},{"id":"2106.03441","title":"Attention Temperature Matters in Abstractive Summarization Distillation","created":"2021-06-07","cited_by_count":14,"area":null},{"id":"2111.10734","title":"Deep Probability Estimation","created":"2021-11-21","cited_by_count":14,"area":null},{"id":"2206.01367","title":"Adversarial Unlearning: Reducing Confidence Along Adversarial Directions","created":"2022-06-03","cited_by_count":14,"area":"safety"},{"id":"2406.04370","title":"Large Language Model Confidence Estimation via Black-Box Access","created":"2024-06-01","cited_by_count":14,"area":null},{"id":"2506.07998","title":"Generative Modeling of Weights: Generalization or Memorization?","created":"2025-06-09","cited_by_count":14,"area":null},{"id":"2103.00243","title":"Searching for Robustness: Loss Learning for Noisy Classification Tasks","created":"2021-02-27","cited_by_count":13,"area":null},{"id":"2103.00528","title":"Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation","created":"2021-02-28","cited_by_count":13,"area":null}],"next_cursor":"100"}