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Curriculum learning (CL) - training using samples that are generated and presented in a meaningful order - was introduced in the machine learning context around a decade ago.
The effects of information order and learning mode on schema abstraction
Renee Elio and John R Anderson · 1984
Earlier work this paper cites.
Generalizing from the use of earlier examples in problem solving
Brian H Ross and Patrick T Kennedy · 1990
Earlier work this paper cites.
Teaching by examples: Implications for the process of category acquisition
Judith Avrahami, Yaakov Kareev, Yonatan Bogot, Ruth Caspi, Salomka Dunaevsky, and Sharon Lerner · 1997
Earlier work this paper cites.
Efficient noise-tolerant learning from statistical queries
Michael Kearns · 1998
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More on average case vs approximation complexity
Michael Alekhnovich · 2003
Earlier work this paper cites.
Learning functions of k relevant variables
Elchanan Mossel, Ryan O’Donnell, and Rocco A Servedio · 2004
Earlier work this paper cites.
Learning dnf from random walks
Nader H Bshouty, Elchanan Mossel, Ryan O’Donnell, and Rocco A Servedio · 2005
Earlier work this paper cites.
Agnostically learning juntas from random walks
Jan Arpe and Elchanan Mossel · 2008
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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
Earlier work this paper cites.
Online learning and online convex optimization
Shai Shalev-Shwartz et al · 2012
Earlier work this paper cites.
K-component recurrent neural network language models using curriculum learning
Yangyang Shi, Martha Larson, and Catholijn M Jonker · 2013
Earlier work this paper cites.
Learning polynomials with neural networks
Alexandr Andoni, Rina Panigrahy, Gregory Valiant, and Li Zhang · 2014
Earlier work this paper cites.
A rational account of pedagogical reasoning: Teaching by, and learning from, examples
Patrick Shafto, Noah D Goodman, and Thomas L Griffiths · 2014
Earlier work this paper cites.
Understanding machine learning: From theory to algorithms
Shai Shalev-Shwartz and Shai Ben-David · 2014
Earlier work this paper cites.
Wojciech Zaremba and Ilya Sutskever · 2014
Cited alongside, same era.
Self-paced curriculum learning
Lu Jiang, Deyu Meng, Qian Zhao, Shiguang Shan, and Alexander G Hauptmann · 2015
Cited alongside, same era.
Recurrent neural network language model adaptation with curriculum learning
Yangyang Shi, Martha Larson, and Catholijn M Jonker · 2015
Cited alongside, same era.
Multi-task curriculum transfer deep learning of clothing attributes
Qi Dong, Shaogang Gong, and Xiatian Zhu · 2017
Cited alongside, same era.
Automated curriculum learning for neural networks
Alex Graves, Marc G Bellemare, Jacob Menick, Remi Munos, and Koray Kavukcuoglu · 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
The staircase property: How hierarchical structure can guide deep learning
Emmanuel Abbe, Enric Boix Adsera, Matthew Brennan, Guy Bresler, and Dheeraj Nagaraj · 2021
Later among the works it cites.
On the power of differentiable learning versus PAC and SQ learning
Emmanuel Abbe, Pritish Kamath, Eran Malach, Colin Sandon, and Nathan Srebro · 2021
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Curriculum learning for language modeling
Daniel Campos · 2021
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Quantifying the benefit of using differentiable learning over tangent kernels
Eran Malach, Pritish Kamath, Emmanuel Abbe, and Nathan Srebro · 2021
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A survey on curriculum learning
Xin Wang, Yudong Chen, and Wenwu Zhu · 2021
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Modeling multi-species rna modification through multi-task curriculum learning
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Cited alongside, same era.
Failures of gradient-based deep learning
Shai Shalev-Shwartz, Ohad Shamir, and Shaked Shammah · 2017
Cited alongside, same era.
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 · 2018
Cited alongside, same era.
Curriculum learning by transfer learning: Theory and experiments with deep networks
Daphna Weinshall, Gad Cohen, and Dan Amir · 2018
Cited alongside, same era.
Sgd on neural networks learns functions of increasing complexity
Dimitris Kalimeris, Gal Kaplun, Preetum Nakkiran, Benjamin Edelman, Tristan Yang, Boaz Barak, and Haofeng Zhang · 2019
Cited alongside, same era.
On the universality of deep learning
Emmanuel Abbe and Colin Sandon · 2020
Cited alongside, same era.
Learning parities with neural networks
Amit Daniely and Eran Malach · 2020
Cited alongside, same era.
Yuanpeng Xiong, Xuan He, Dan Zhao, Tingzhong Tian, Lixiang Hong, Tao Jiang, and Jianyang Zeng · 2021
Later among the works it cites.
The merged-staircase property: a necessary and nearly sufficient condition for sgd learning of sparse functions on two-layer neural networks
Emmanuel Abbe, Enric Boix Adsera, and Theodor Misiakiewicz · 2022
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On the non-universality of deep learning: quantifying the cost of symmetry
Emmanuel Abbe and Enric Boix-Adsera · 2022
Later among the works it cites.
An initial alignment between neural network and target is needed for gradient descent to learn
Emmanuel Abbe, Elisabetta Cornacchia, Jan Hazla, and Christopher Marquis · 2022
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Hidden progress in deep learning: Sgd learns parities near the computational limit
Boaz Barak, Benjamin L Edelman, Surbhi Goel, Sham Kakade, Eran Malach, and Cyril Zhang · 2022
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Neural networks trained with sgd learn distributions of increasing complexity
Maria Refinetti, Alessandro Ingrosso, and Sebastian Goldt · 2022
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Curriculum learning: A survey
Petru Soviany, Radu Tudor Ionescu, Paolo Rota, and Nicu Sebe · 2022
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An analytical theory of curriculum learning in teacher–student networks
Luca Saglietti, Stefano Sarao Mannelli, and Andrew Saxe · 2022
Later among the works it cites.
Generalization on the unseen, logic reasoning and degree curriculum
Emmanuel Abbe, Samy Bengio, Aryo Lotfi, and Kevin Rizk · 2023
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