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Curriculum Learning - the idea of teaching by gradually exposing the learner to examples in a meaningful order, from easy to hard, has been investigated in the context of machine learning long ago.
The need for biases in learning generalizations
Tom M Mitchell · 1980
Earlier work this paper cites.
The behavior of organisms: An experimental analysis
Burrhus Frederic Skinner · 1990
Earlier work this paper cites.
Learning and development in neural networks: The importance of starting small
Jeffrey L Elman · 1993
Earlier work this paper cites.
Neural network learning control of robot manipulators using gradually increasing task difficulty
Terence D Sanger · 1994
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Boosting the margin: A new explanation for the effectiveness of voting methods
Robert E. Schapire, Yoav Freund, Peter Bartlett, and Wee Sun Lee · 1998
Earlier work this paper cites.
The discipline of machine learning , volume 9
Tom Michael Mitchell · 2006
Cited alongside, same era.
Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
Cited alongside, same era.
Self-paced learning for latent variable models
M Pawan Kumar, Benjamin Packer, and Daphne Koller · 2010
Cited alongside, same era.
Action-conditional video prediction using deep networks in atari games
Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard L Lewis, and Satinder Singh · 2015
Cited alongside, same era.
Facenet: A unified embedding for face recognition and clustering
Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
Cited alongside, same era.
Basic level categorization facilitates visual object recognition
Panqu Wang and Garrison W Cottrell · 2015
Later among the works it cites.
Training region-based object detectors with online hard example mining
Abhinav Shrivastava, Abhinav Gupta, and Ross Girshick · 2016
Later among the works it cites.
Active bias: Training more accurate neural networks by emphasizing high variance samples
Haw-Shiuan Chang, Erik Learned-Miller, and Andrew McCallum · 2017
Later among the works it cites.
Mentornet: Regularizing very deep neural networks on corrupted labels
Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, and Li Fei-Fei · 2017
Later among the works it cites.
Curriculum learning by transfer learning: Theory and experiments with deep networks
Daphna Weinshall, Gad Cohen, and Dan Amir · 2018
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