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Inspired by human learning, researchers have proposed ordering examples during training based on their difficulty.
Beyond synthetic noise: Deep learning on controlled noisy labels
Lu Jiang, Di Huang, Mason Liu, and Weilong Yang · 1911
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Learning and development in neural networks: The importance of starting small
Jeffrey L Elman · 1993
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Neural network learning control of robot manipulators using gradually increasing task difficulty
Terence D Sanger · 1994
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Teaching by examples: Implications for the process of category acquisition
Judith Avrahami, Yaakov Kareev, Yonatan Bogot, Ruth Caspi, Salomka Dunaevsky, and Sharon Lerner · 1997
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Catastrophic forgetting in connectionist networks
Robert M French · 1999
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Improving predictive inference under covariate shift by weighting the log-likelihood function
Hidetoshi Shimodaira · 2000
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Exploring the memorization-generalization continuum in deep learning
Ziheng Jiang, Chiyuan Zhang, Kunal Talwar, and Michael C Mozer · 2002
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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
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Covariate shift by kernel mean matching
Arthur Gretton, Alex Smola, Jiayuan Huang, Marcel Schmittfull, Karsten Borgwardt, and Bernhard Schölkopf · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Self-paced learning for latent variable models
M Pawan Kumar, Benjamin Packer, and Daphne Koller · 2010
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How do humans teach: On curriculum learning and teaching dimension
Faisal Khan, Bilge Mutlu, and Jerry Zhu · 2011
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Learning with noisy labels
Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari · 2013
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Food-101–mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
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An empirical investigation of catastrophic forgeting in gradientbased neural networks
Ian J Goodfellow, Mehdi Mirza, Aaron Courville Da Xiao, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Stochastic gradient descent, weighted sampling, and the randomized kaczmarz algorithm
Deanna Needell, Rachel Ward, and Nati Srebro · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Learning from noisy labels with deep neural networks
Sainbayar Sukhbaatar and Rob Fergus · 2014
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Wojciech Zaremba and Ilya Sutskever · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Self-paced curriculum learning
Lu Jiang, Deyu Meng, Qian Zhao, Shiguang Shan, and Alexander G Hauptmann · 2015
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Curriculum learning of multiple tasks
Anastasia Pentina, Viktoriia Sharmanska, and Christoph H Lampert · 2015
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Visualizing and understanding curriculum learning for long short-term memory networks
Volkan Cirik, Eduard Hovy, and Louis-Philippe Morency · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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A closer look at memorization in deep networks
Devansh Arpit, Stanislaw Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al · 2017
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Learning with average top-k loss
Yanbo Fan, Siwei Lyu, Yiming Ying, and Baogang Hu · 2017
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Automated curriculum learning for neural networks
Alex Graves, Marc G Bellemare, Jacob Menick, Rémi Munos, and Koray Kavukcuoglu · 2017
Cited alongside, same era.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
Accelerating deep learning by focusing on the biggest losers
Angela H Jiang, Daniel L-K Wong, Giulio Zhou, David G Andersen, Jeffrey Dean, Gregory R Ganger, Gauri Joshi, Michael Kaminksy, Michael Kozuch, Zachary C Lipton, et al · 2019
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Do deep neural networks learn shallow learnable examples first?
Karttikeya Mangalam and Vinay Uday Prabhu · 2019
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Teacher-student curriculum learning
Tambet Matiisen, Avital Oliver, Taco Cohen, and John Schulman · 2019
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Toward understanding catastrophic forgetting in continual learning
Cuong V Nguyen, Alessandro Achille, Michael Lam, Tal Hassner, Vijay Mahadevan, and Stefano Soatto · 2019
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Towards robust learning with different label noise distributions
Diego Ortego, Eric Arazo, Paul Albert, Noel E O’Connor, and Kevin McGuinness · 2019
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Curriculum learning and minibatch bucketing in neural machine translation
Tom Kocmi and Ondřej Bojar · 2017
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Webvision database: Visual learning and understanding from web data
Wen Li, Limin Wang, Wei Li, Eirikur Agustsson, and Luc Van Gool · 2017
Cited alongside, same era.
Curriculum learning for multi-task classification of visual attributes
Nikolaos Sarafianos, Theodore Giannakopoulos, Christophoros Nikou, and Ioannis A Kakadiaris · 2017
Cited alongside, same era.
Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2017
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Optimization methods for large-scale machine learning
Léon Bottou, Frank E Curtis, and Jorge Nocedal · 2018
Cited alongside, same era.
Learning and memorization
Satrajit Chatterjee · 2018
Cited alongside, same era.
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Competence-based curriculum learning for neural machine translation
Emmanouil Antonios Platanios, Otilia Stretcu, Graham Neubig, Barnabas Poczos, and Tom Mitchell · 2019
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2019
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Data parameters: A new family of parameters for learning a differentiable curriculum
Shreyas Saxena, Oncel Tuzel, and Dennis DeCoste · 2019
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Learning with bad training data via iterative trimmed loss minimization
Yanyao Shen and Sujay Sanghavi · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
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An empirical study of example forgetting during deep neural network learning
Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, and Geoffrey J Gordon · 2019
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Dynamic curriculum learning for imbalanced data classification
Yiru Wang, Weihao Gan, Jie Yang, Wei Wu, and Junjie Yan · 2019
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Small relu networks are powerful memorizers: a tight analysis of memorization capacity
Chulhee Yun, Suvrit Sra, and Ali Jadbabaie · 2019
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Curriculum learning for domain adaptation in neural machine translation
Xuan Zhang, Pamela Shapiro, Gaurav Kumar, Paul McNamee, Marine Carpuat, and Kevin Duh · 2019
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Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Curricularface: adaptive curriculum learning loss for deep face recognition
Yuge Huang, Yuhan Wang, Ying Tai, Xiaoming Liu, Pengcheng Shen, Shaoxin Li, Jilin Li, and Feiyue Huang · 2020
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Ordered sgd: A new stochastic optimization framework for empirical risk minimization
Kenji Kawaguchi and Haihao Lu · 2020
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Uniform convergence of rank-weighted learning
Justin Khim, Liu Leqi, Adarsh Prasad, and Pradeep Ravikumar · 2020
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Curriculum loss: Robust learning and generalization against label corruption
Yueming Lyu and Ivor W. Tsang · 2020
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Anatomy of catastrophic forgetting: Hidden representations and task semantics
Vinay V Ramasesh, Ethan Dyer, and Maithra Raghu · 2020
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Caliban: Docker-based job manager for reproducible workflows
Sam Ritchie, Ambrose Slone, and Vinay Ramasesh · 2020
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Choosing the sample with lowest loss makes sgd robust
Vatsal Shah, Xiaoxia Wu, and Sujay Sanghavi · 2020
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Curriculum for reinforcement learning
Lilian Weng · 2020
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Identity crisis: Memorization and generalization under extreme overparameterization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Michael C. Mozer, and Yoram Singer · 2020
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