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Machine learning (ML) models are only as good as the data they are trained on.
A comparison of alternative tests of significance for the problem of m rankings
Milton Friedman · 1940
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Individual comparisons by ranking methods
Frank Wilcoxon · 1945
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A simple sequentially rejective multiple test procedure
Sture Holm · 1979
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Concept Acquisition Through Representational Adjustment (Technical Report 87-19)
J.S. Schlimmer · 1987
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Knowledge acquisition and explanation for multi-attribute decision making
Marko Bohanec and Vladislav Rajkovi · 1988
Earlier work this paper cites.
Combining multiple representations and classifiers for pen-based handwritten digit recognition
F. Alimoglu and E. Alpaydin · 1997
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The impact of the MIT-BIH arrhythmia database
George B. Moody and Roger G. Mark · 2001
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Class noise vs. attribute noise: A quantitative study
Xingquan Zhu and Xindong Wu · 2004
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Statistical comparisons of classifiers over multiple data sets
Janez Demšar · 2006
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A survey of crowdsourcing systems
Man-Ching Yuen, Irwin King, and Kwong-Sak Leung · 2011
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Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts · 2011
Earlier work this paper cites.
Classification of household devices by electricity usage profiles
Jason Lines, Anthony Bagnall, Patrick Caiger-Smith, and Simon Anderson · 2011
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Classification in the presence of label noise: a survey
Benoît Frénay and Michel Verleysen · 2013
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Analyzing the presence of noise in multi-class problems: alleviating its influence with the one-vs-one decomposition
José A Sáez, Mikel Galar, Julián Luengo, and Francisco Herrera · 2014
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
Earlier work this paper cites.
Training deep neural networks on noisy labels with bootstrapping
Scott E. Reed, Honglak Lee, Dragomir Anguelov, Christian Szegedy, Dumitru Erhan, and Andrew Rabinovich · 2015
Earlier work this paper cites.
Classification with noisy labels by importance reweighting
T. Liu and D. Tao · 2015
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A Rate of Convergence for Mixture Proportion Estimation, with Application to Learning from Noisy Labels
Clayton Scott · 2015
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Learning from massive noisy labeled data for image classification
Tong Xiao, Tian Xia, Yi Yang, Chang Huang, and Xiaogang Wang · 2015
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Calibrating probability with undersampling for unbalanced classification
Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson, and Gianluca Bontempi · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
How we analyzed the compas recidivism algorithm, 05 2016
Lauren Kirchner Jeff Larson, Surya Mattu and Julia Angwin · 2016
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Should we really use post-hoc tests based on mean-ranks?
Alessio Benavoli, Giorgio Corani, and Francesca Mangili · 2016
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Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs
Varun Gulshan, Lily Peng, Marc Coram, Martin C. Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams, Jorge Cuadros, Ramasamy Kim, Rajiv Raman, Philip C. Nelson, Jessica L. Mega, and Dale R. Webster · 2016
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
Earlier work this paper cites.
Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
Earlier work this paper cites.
A closer look at memorization in deep networks
Devansh Arpit, Stanisław Jastrzębski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al · 2017
Earlier work this paper cites.
Learning with confident examples: Rank pruning for robust classification with noisy labels
Curtis G. Northcutt, Tailin Wu, and Isaac L. Chuang · 2017
Earlier work this paper cites.
Making deep neural networks robust to label noise: A loss correction approach
Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu · 2017
Earlier work this paper cites.
Toward robustness against label noise in training deep discriminative neural networks
Arash Vahdat · 2017
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Robust loss functions under label noise for deep neural networks
Aritra Ghosh, Himanshu Kumar, and P. S. Sastry · 2017
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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Indexing and classifying gigabytes of time series under time warping
Chang Wei Tan, Geoffrey I. Webb, and Francois Petitjean · 2017
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LSTM fully convolutional networks for time series classification
Fazle Karim, Somshubra Majumdar, Houshang Darabi, and Shun Chen · 2017
Cited alongside, same era.
The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances
Anthony Bagnall, Jason Lines, Aaron Bostrom, James Large, and Eamonn Keogh · 2017
Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers
Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, and Ming Zhou · 2020
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Tabtransformer: Tabular data modeling using contextual embeddings, 2020
Xin Huang, Ashish Khetan, Milan Cvitkovic, and Zohar Karnin · 2020
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Label noise types and their effects on deep learning
Görkem Algan and Ilkay Ulusoy · 2020
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Fantastic generalization measures and where to find them
Yiding Jiang, Behnam Neyshabur, Hossein Mobahi, Dilip Krishnan, and Samy Bengio · 2020
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Weak supervision for affordable modeling of electrocardiogram data
Mononito Goswami, Benedikt Boecking, and Artur Dubrawski · 2021
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MentorNet: Learning data-driven curriculum for very deep neural networks on corrupted labels
Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, and Li Fei-Fei · 2018
Cited alongside, same era.
Co-teaching: Robust training of deep neural networks with extremely noisy labels
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor W. Tsang, and Masashi Sugiyama · 2018
Cited alongside, same era.
The UEA multivariate time series classification archive, 2018, 2018
Anthony Bagnall, Hoang Anh Dau, Jason Lines, Michael Flynn, James Large, Aaron Bostrom, Paul Southam, and Eamonn Keogh · 2018
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding, 2019
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Human uncertainty makes classification more robust
Joshua C Peterson, Ruairidh M Battleday, Thomas L Griffiths, and Olga Russakovsky · 2019
Cited alongside, same era.
Are anchor points really indispensable in label-noise learning?
Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, and Masashi Sugiyama · 2019
Cited alongside, same era.
Early detection of sepsis with machine learning techniques: a brief clinical perspective
Daniele Roberto Giacobbe, Alessio Signori, Filippo Del Puente, Sara Mora, Luca Carmisciano, Federica Briano, Antonio Vena, Lorenzo Ball, Chiara Robba, Paolo Pelosi, et al · 2021
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Understanding deep learning (still) requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2021
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Fair classification with group-dependent label noise
Jialu Wang, Yang Liu, and Caleb Levy · 2021
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Interactive label cleaning with example-based explanations
Stefano Teso, Andrea Bontempelli, Fausto Giunchiglia, and Andrea Passerini · 2021
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Clusterability as an alternative to anchor points when learning with noisy labels
Zhaowei Zhu, Yiwen Song, and Yang Liu · 2021
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Meta label correction for noisy label learning
Guoqing Zheng, Ahmed Hassan Awadallah, and Susan Dumais · 2021
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Learning with instance-dependent label noise: A sample sieve approach
Hao Cheng, Zhaowei Zhu, Xingyu Li, Yifei Gong, Xing Sun, and Yang Liu · 2021
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Characterizing the uncertainty of label noise in systematic ultrasound-guided prostate biopsy
Golara Javadi, Samareh Samadi, Sharareh Bayat, Samira Sojoudi, Antonio Hurtado, Silvia Chang, Peter Black, Parvin Mousavi, and Purang Abolmaesumi · 2021
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A reconstruction error-based framework for label noise detection
Zahra Salekshahrezaee, Joffrey Leevy, and Taghi Khoshgoftaar · 2021
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A robustly optimized BERT pre-training approach with post-training
Liu Zhuang, Lin Wayne, Shi Ya, and Zhao Jun · 2021
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Model-Centric Verification of Artificial Intelligence
Nicholas Gisolfi · 2021
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Analysing the noise model error for realistic noisy label data
Michael A. Hedderich, D. Zhu, and Dietrich Klakow · 2021
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Learning from multiple annotator noisy labels via sample-wise label fusion
Zhengqi Gao, Fan-Keng Sun, Mingran Yang, Sucheng Ren, Zikai Xiong, Marc Engeler, Antonio Burazer, Linda Wildling, Luca Daniel, and Duane S Boning · 2022
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Fair classification with instance-dependent label noise
Songhua Wu, Mingming Gong, Bo Han, Yang Liu, and Tongliang Liu · 2022
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Active label cleaning for improved dataset quality under resource constraints
Melanie Bernhardt, Daniel Coelho de Castro, Ryutaro Tanno, Anton Schwaighofer, Kerem Tezcan, Miguel Monteiro, Shruthi Bannur, Matthew Lungren, Aditya Nori, Ben Glocker, Javier Alvarez-Valle, and Ozan Oktay · 2022
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Detecting label errors by using pre-trained language models
Derek Chong, Jenny Hong, and Christopher Manning · 2022
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Learning with noisy labels revisited: A study using real-world human annotations
Jiaheng Wei, Zhaowei Zhu, Hao Cheng, Tongliang Liu, Gang Niu, and Yang Liu · 2022
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Model-agnostic label quality scoring to detect real-world label errors
Johnson Kuan and Jonas Mueller · 2022
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Ctrl: Clustering training losses for label error detection, 2022
Chang Yue and Niraj K. Jha · 2022
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Is BERT robust to label noise? a study on learning with noisy labels in text classification
Dawei Zhu, Michael A. Hedderich, Fangzhou Zhai, David Adelani, and Dietrich Klakow · 2022
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Weak supervision for affordable modeling of electrocardiogram data, 2022
Mononito Goswami, Benedikt Boecking, and Artur Dubrawski · 2022
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Fastvit: A fast hybrid vision transformer using structural reparameterization, 2023
Pavan Kumar Anasosalu Vasu, James Gabriel, Jeff Zhu, Oncel Tuzel, and Anurag Ranjan · 2023
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A time series is worth 64 words: Long-term forecasting with transformers
Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam · 2023
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