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Gradient-based meta-learning approaches have been successful in few-shot learning, transfer learning, and a wide range of other domains.
Learning Representations by Back-propagating Errors
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams · 1986
Earlier work this paper cites.
Evolutionary principles in self-referential learning
Jürgen Schmidhuber · 1987
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Using fast weights to deblur old memories
Geoffrey E Hinton and David C Plaut · 1987
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A theoretical framework for back-propagation
Yann LeCun · 1988
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Some bounds on the complexity of gradients, jacobians, and hessians
Andreas Griewank · 1993
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Gradient-Based Optimization of Hyper-parameters
Yoshua Bengio · 2000
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Learning to learn using gradient descent
Sepp Hochreiter, A Steven Younger, and Peter R Conwell · 2001
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L. Li, Kai Li, and Li Fei-Fei · 2009
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Recognizing indoor scenes
Ariadna Quattoni and Antonio Torralba · 2009
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Deep learning via Hessian-free optimization
James Martens · 2010
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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An analysis of single-layer networks in unsupervised feature learning
Adam Coates, Andrew Ng, and Honglak Lee · 2011
Earlier work this paper cites.
Human action recognition by learning bases of action attributes and parts
Bangpeng Yao, Xiaoye Jiang, Aditya Khosla, Andy Lai Lin, Leonidas Guibas, and Li Fei-Fei · 2011
Earlier work this paper cites.
Novel dataset for fine-grained image categorization
Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao, and Li Fei-Fei · 2011
Earlier work this paper cites.
One shot learning of simple visual concepts
Brenden M. Lake, Ruslan Salakhutdinov, Jason Gross, and Joshua B. Tenenbaum · 2011
Earlier work this paper cites.
Generic methods for optimization-based modeling
Justin Domke · 2012
Cited alongside, same era.
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, and Others · 2014
Cited alongside, same era.
Gradient-based hyperparameter optimization through reversible learning
Dougal Maclaurin, David Duvenaud, and Ryan Adams · 2015
Cited alongside, same era.
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan and Andrew Zisserman · 2015
Cited alongside, same era.
Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba · 2015
Cited alongside, same era.
Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gómez Colmenarejo, Matthew W. Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando De Freitas · 2016
Learning to compare: Relation network for few-shot learning
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales · 2018
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Few-shot learning with graph neural networks
Victor Garcia and Joan Bruna · 2018
Later among the works it cites.
Bilevel Programming for Hyperparameter Optimization and Meta-Learning
Luca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, and Massimiliano Pontil · 2018
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On First-Order Meta-Learning Algorithms
Alex Nichol, Joshua Achiam, and John Schulman · 2018
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Knowledge transfer with jacobian matching
Suraj Srinivas and François Fleuret · 2018
Later among the works it cites.
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Cited alongside, same era.
Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al · 2016
Cited alongside, same era.
Hyperparameter optimization with approximate gradient
Fabian Pedregosa · 2016
Cited alongside, same era.
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
Cited alongside, same era.
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Cited alongside, same era.
Muhammad Abdullah Jamal, Guo-Jun Qi, and Mubarak Shah · 2018
Later among the works it cites.
Learning What and Where to Transfer
Yunhun Jang, Hankook Lee, Sung Ju Hwang, and Jinwoo Shin · 2019
Later among the works it cites.
Meta-Learning with Implicit Gradients
Aravind Rajeswaran, Chelsea Finn, Sham Kakade, and Sergey Levine · 2019
Later among the works it cites.
On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms
Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar · 2019
Later among the works it cites.
Tiny imagenet visual recognition challenge
Stanford CS231N · 2019
Later among the works it cites.
How to train your MAML
Antreas Antoniou, Harrison Edwards, and Amos Storkey · 2019
Later among the works it cites.
Optimizing Millions of Hyperparameters by Implicit Differentiation
Jonathan Lorraine, Paul Vicol, and David Duvenaud · 2020
Closest in time.
Meta Learning in Neural Networks : A Survey
Timothy Hospedales, Antreas Antoniou, Paul Micaelli, and Amos Storkey · 2020
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Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML
Aniruddh Raghu, Maithra Raghu, Samy Bengio, and Oriol Vinyals · 2020
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ES-MAML: Simple Hessian-Free Meta Learning
Xingyou Song, Wenbo Gao, Yuxiang Yang, Krzysztof Choromanski, Aldo Pacchiano, and Yunhao Tang · 2020
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Multi-Step Model-Agnostic Meta-Learning: Convergence and Improved Algorithms
Kaiyi Ji, Junjie Yang, and Yingbin Liang · 2020
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