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Few-shot meta-learning methods consider the problem of learning new tasks from a small, fixed number of examples, by meta-learning across static data from a set of previous tasks.
Infinite mixture prototypes for few-shot learning
Kelsey R Allen, Evan Shelhamer, Hanul Shin, and Joshua B Tenenbaum · 1902
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Approximation to bayes risk in repeated play
James Hannan · 1957
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Evolutionary principles in self-referential learning
Jurgen Schmidhuber · 1987
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On the optimization of a synaptic learning rule
Samy Bengio, Yoshua Bengio, Jocelyn Cloutier, and Jan Gecsei · 1992
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Lifelong learning algorithms
Sebastian Thrun · 1998
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Catastrophic forgetting in connectionist networks
Robert M French · 1999
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Learning to learn using gradient descent
Sepp Hochreiter, A Steven Younger, and Peter R Conwell · 2001
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Efficient algorithms for online decision problems
Adam Tauman Kalai and Santosh Vempala · 2005
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One shot learning of simple visual concepts
Brenden M Lake, Ruslan Salakhutdinov, Jason Gross, and Joshua B Tenenbaum · 2011
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Online learning and online convex optimization
Shai Shalev-Shwartz · 2012
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Beyond pascal: A benchmark for 3d object detection in the wild
Yu Xiang, Roozbeh Mottaghi, and Silvio Savarese · 2014
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Siamese neural networks for one-shot image recognition
Gregory Koch · 2015
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Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando De Freitas · 2016
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Rl2: Fast reinforcement learning via slow reinforcement learning
Yan Duan, John Schulman, Xi Chen, Peter L Bartlett, Ilya Sutskever, and Pieter Abbeel · 2016
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Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2016
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Meta-learning with memory-augmented neural networks
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lillicrap · 2016
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A deep hierarchical approach to lifelong learning in minecraft
Chen Tessler, Shahar Givony, Tom Zahavy, Daniel J Mankowitz, and Shie Mannor · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, and Daan Wierstra · 2016
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Learning to reinforcement learn
Jane X Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z Leibo, Remi Munos, Charles Blundell, Dharshan Kumaran, and Matt Botvinick · 2016
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Continuous adaptation via meta-learning in nonstationary and competitive environments
Maruan Al-Shedivat, Trapit Bansal, Yuri Burda, Ilya Sutskever, Igor Mordatch, and Pieter Abbeel · 2017
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Machine learning and prediction in medicine—beyond the peak of inflated expectations
Jonathan H Chen and Steven M Asch · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 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
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Learning without forgetting
Zhizhong Li and Derek Hoiem · 2017
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Gradient episodic memory for continual learning
David Lopez-Paz et al · 2017
Cited alongside, same era.
Online-within-online meta-learning
Giulia Denevi, Dimitris Stamos, Carlo Ciliberto, and Massimiliano Pontil · 2019
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Online meta-learning
Chelsea Finn, Aravind Rajeswaran, Sham Kakade, and Sergey Levine · 2019
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Modulating transfer between tasks in gradient-based meta-learning, 2019
Erin Grant, Ghassen Jerfel, Katherine Heller, and Thomas L. Griffiths · 2019
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Continuous meta-learning without tasks
James Harrison, Apoorva Sharma, Chelsea Finn, and Marco Pavone · 2019
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Task agnostic continual learning via meta learning
Xu He, Jakub Sygnowski, Alexandre Galashov, Andrei A Rusu, Yee Whye Teh, and Razvan Pascanu · 2019
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Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel · 2017
Cited alongside, same era.
Meta networks
Tsendsuren Munkhdalai and Hong Yu · 2017
Cited alongside, same era.
Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2017
Cited alongside, same era.
icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, and Christoph H Lampert · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
Cited alongside, same era.
Mark Woodward and Chelsea Finn · 2017
Cited alongside, same era.
Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
Cited alongside, same era.
Khurram Javed and Martha White · 2019
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Provable guarantees for gradient-based meta-learning
Mikhail Khodak, Maria-Florina Balcan, and Ameet Talwalkar · 2019
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Meta-learning with differentiable convex optimization
Kwonjoon Lee, Subhransu Maji, Avinash Ravichandran, and Stefano Soatto · 2019
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Meta-learning with implicit gradients
Aravind Rajeswaran, Chelsea Finn, Sham M Kakade, and Sergey Levine · 2019
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Experience replay for continual learning
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, and Gregory Wayne · 2019
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Meta-dataset: A dataset of datasets for learning to learn from few examples
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, and Hugo Larochelle · 2019
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Meta-learning without memorization
Mingzhang Yin, George Tucker, Mingyuan Zhou, Sergey Levine, and Chelsea Finn · 2019
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Recommending what video to watch next: a multitask ranking system
Zhe Zhao, Lichan Hong, Li Wei, Jilin Chen, Aniruddh Nath, Shawn Andrews, Aditee Kumthekar, Maheswaran Sathiamoorthy, Xinyang Yi, and Ed Chi · 2019
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Online meta-learning on non-convex setting
Zhenxun Zhuang, Yunlong Wang, Kezi Yu, and Songtao Lu · 2019
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Defining benchmarks for continual few-shot learning
Antreas Antoniou, Massimiliano Patacchiola, Mateusz Ochal, and Amos Storkey · 2020
Closest in time.
A theoretical analysis of the number of shots in few-shot learning
Tianshi Cao, Marc T Law, and Sanja Fidler · 2020
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Wandering within a world: Online contextualized few-shot learning
Mengye Ren, Michael L Iuzzolino, Michael C Mozer, and Richard S Zemel · 2020
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Few-shot class-incremental learning
Xiaoyu Tao, Xiaopeng Hong, Xinyuan Chang, Songlin Dong, Xing Wei, and Yihong Gong · 2020
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Are we overfitting to experimental setups in recognition?, 2020
Matthew Wallingford, Aditya Kusupati, Keivan Alizadeh-Vahid, Aaron Walsman, Aniruddha Kembhavi, and Ali Farhadi · 2020
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Online structured meta-learning
Huaxiu Yao, Yingbo Zhou, Mehrdad Mahdavi, Zhenhui Jessie Li, Richard Socher, and Caiming Xiong · 2020
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