{"list":"other","total":60,"rows":[{"id":"2203.02155","title":"Training language models to follow instructions with human feedback","created":"2022-03-04","cited_by_count":15971},{"id":"2204.05862","title":"Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback","created":"2022-04-12","cited_by_count":3720},{"id":"1703.04730","title":"Understanding Black-box Predictions via Influence Functions","created":"2017-03-14","cited_by_count":1741},{"id":"1706.05394","title":"A Closer Look at Memorization in Deep Networks","created":"2017-06-16","cited_by_count":942},{"id":"1910.09457","title":"Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods","created":"2019-10-21","cited_by_count":913},{"id":"2002.07394","title":"DivideMix: Learning with Noisy Labels as Semi-supervised Learning","created":"2020-02-18","cited_by_count":692},{"id":"1812.05159","title":"An Empirical Study of Example Forgetting during Deep Neural Network Learning","created":"2018-12-12","cited_by_count":678},{"id":"1609.03683","title":"Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach","created":"2016-09-13","cited_by_count":665},{"id":"1712.05055","title":"MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels","created":"2017-12-14","cited_by_count":653},{"id":"1904.02868","title":"Data Shapley: Equitable Valuation of Data for Machine Learning","created":"2019-04-05","cited_by_count":629},{"id":"2007.08199","title":"Learning from Noisy Labels with Deep Neural Networks: A Survey","created":"2020-07-16","cited_by_count":582},{"id":"2002.08484","title":"Estimating Training Data Influence by Tracing Gradient Descent","created":"2020-02-19","cited_by_count":580},{"id":"2107.07075","title":"Deep Learning on a Data Diet: Finding Important Examples Early in Training","created":"2021-07-15","cited_by_count":533},{"id":"2107.09044","title":"Just Train Twice: Improving Group Robustness without Training Group Information","created":"2021-07-19","cited_by_count":523},{"id":"2008.03703","title":"What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation","created":"2020-08-09","cited_by_count":497},{"id":"2004.05785","title":"Learning under Concept Drift: A Review","created":"2020-04-13","cited_by_count":477},{"id":"1906.05271","title":"Does Learning Require Memorization? A Short Tale about a Long Tail","created":"2019-06-12","cited_by_count":460},{"id":"1911.00068","title":"Confident Learning: Estimating Uncertainty in Dataset Labels","created":"2019-10-31","cited_by_count":413},{"id":"2103.14749","title":"Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks","created":"2021-03-26","cited_by_count":395},{"id":"1803.11364","title":"Joint Optimization Framework for Learning with Noisy Labels","created":"2018-03-30","cited_by_count":293},{"id":"2001.10528","title":"Identifying Mislabeled Data using the Area Under the Margin Ranking","created":"2020-01-28","cited_by_count":261},{"id":"1605.07723","title":"Data Programming: Creating Large Training Sets, Quickly","created":"2016-05-25","cited_by_count":246},{"id":"1905.05040","title":"Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels","created":"2019-05-13","cited_by_count":209},{"id":"1906.00189","title":"Are Anchor Points Really Indispensable in Label-Noise Learning?","created":"2019-06-01","cited_by_count":208},{"id":"1811.09720","title":"Representer Point Selection for Explaining Deep Neural Networks","created":"2018-11-23","cited_by_count":187},{"id":"1806.02612","title":"Dimensionality-Driven Learning with Noisy Labels","created":"2018-06-07","cited_by_count":173},{"id":"1911.05248","title":"What Do Compressed Deep Neural Networks Forget?","created":"2019-11-13","cited_by_count":171},{"id":"2110.14049","title":"Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning","created":"2021-10-26","cited_by_count":165},{"id":"2010.02347","title":"Learning with Instance-Dependent Label Noise: A Sample Sieve Approach","created":"2020-10-05","cited_by_count":158},{"id":"1704.07433","title":"Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples","created":"2017-04-24","cited_by_count":147},{"id":"2209.05364","title":"If Influence Functions are the Answer, Then What is the Question?","created":"2022-09-12","cited_by_count":144},{"id":"1910.03231","title":"Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates","created":"2019-10-08","cited_by_count":142},{"id":"2006.07805","title":"Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning","created":"2020-06-14","cited_by_count":142},{"id":"2106.09647","title":"Deep Learning Through the Lens of Example Difficulty","created":"2021-06-17","cited_by_count":141},{"id":"2011.04406","title":"A Survey of Label-noise Representation Learning: Past, Present and Future","created":"2020-11-09","cited_by_count":122},{"id":"2008.11600","title":"Estimating Example Difficulty Using Variance of Gradients","created":"2020-08-26","cited_by_count":97},{"id":"2109.11377","title":"WRENCH: A Comprehensive Benchmark for Weak Supervision","created":"2021-09-23","cited_by_count":96},{"id":"2002.03206","title":"Characterizing Structural Regularities of Labeled Data in Overparameterized Models","created":"2020-02-08","cited_by_count":93},{"id":"2110.06283","title":"Detecting Corrupted Labels Without Training a Model to Predict","created":"2021-10-12","cited_by_count":58},{"id":"2004.12289","title":"Deep k-NN for Noisy Labels","created":"2020-04-26","cited_by_count":55},{"id":"2010.03179","title":"Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages","created":"2020-10-07","cited_by_count":52},{"id":"2207.10276","title":"ProMix: Combating Label Noise via Maximizing Clean Sample Utility","created":"2022-07-21","cited_by_count":48},{"id":"2103.04173","title":"LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environment","created":"2021-03-06","cited_by_count":40},{"id":"2202.02016","title":"Identifiability of Label Noise Transition Matrix","created":"2022-02-04","cited_by_count":34},{"id":"2107.02233","title":"End-to-End Weak Supervision","created":"2021-07-05","cited_by_count":33},{"id":"2105.13001","title":"Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network","created":"2021-05-27","cited_by_count":30},{"id":"2311.11202","title":"Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models","created":"2023-11-19","cited_by_count":26},{"id":"2210.13043","title":"Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data","created":"2022-10-24","cited_by_count":22},{"id":"2402.10723","title":"Conformalized Credal Set Predictors","created":"2024-02-16","cited_by_count":21},{"id":"2107.13098","title":"A Tale Of Two Long Tails","created":"2021-07-27","cited_by_count":19},{"id":"2208.09833","title":"Label-Noise Learning with Intrinsically Long-Tailed Data","created":"2022-08-21","cited_by_count":17},{"id":"2210.06516","title":"How to Sift Out a Clean Data Subset in the Presence of Data Poisoning?","created":"2022-10-12","cited_by_count":17},{"id":"2308.13862","title":"Late Stopping: Avoiding Confidently Learning from Mislabeled Examples","created":"2023-08-26","cited_by_count":17},{"id":"2212.01674","title":"CrossSplit: Mitigating Label Noise Memorization through Data Splitting","created":"2022-12-03","cited_by_count":12},{"id":"2012.09632","title":"From Weakly Supervised Learning to Biquality Learning: an Introduction","created":"2020-12-16","cited_by_count":10},{"id":"2310.01765","title":"Data Cleaning and Machine Learning: A Systematic Literature Review","created":"2023-10-03","cited_by_count":9},{"id":"2209.09658","title":"Lazy vs hasty: linearization in deep networks impacts learning schedule based on example difficulty","created":"2022-09-19","cited_by_count":5},{"id":"2306.04502","title":"Learning with Noisy Labels by Adaptive Gradient-Based Outlier Removal","created":"2023-06-07","cited_by_count":4},{"id":"1403.1893","title":"Becoming More Robust to Label Noise with Classifier Diversity","created":"2014-03-07","cited_by_count":1},{"id":"2409.08647","title":"Training Gradient Boosted Decision Trees on Tabular Data Containing Label Noise for Classification Tasks","created":"2024-09-13","cited_by_count":1}],"next_cursor":null}