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This article reviews meta-learning also known as learning-to-learn which seeks rapid and accurate model adaptation to unseen tasks with applications in highly automated AI, few-shot learning, natural language processing and robotics.
A method for solving the convex programming problem with convergence rate O(1/kˆ2)
Y. NESTEROV · 1983
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Evolutionary principles in self-referential learning (Diploma Thesis)
Jürgen Schmidthuber · 1987
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Making the World Differentiable : On Using Self-Supervised Fully Recurrent Neural Networks for Dynamic Reinforcement Learning and Planning in Non-Stationary Environments ( TR FKI-126-90 )
Jürgen Schmidhuber · 1990
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Principles of metareasoning
Stuart Russell and Eric Wefald · 1991
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A Neural Network the Embeds its own Meta-Levels
Jürgen Schmidhuber · 1993
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Building a Large Annotated Corpus of English: The Penn Treebank
M. Marcus, B. Santorini, and M. Marcinkiewicz · 1993
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A Review of Evolutionary Artificial Neural Networks
Xin Yao · 1993
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On Learning how to Learn Learning Strategies
Jürgen H. Schmidhuber · 1994
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Task-decomposition via plan parsing
Anthony Barrett and Daniel S. Weld · 1994
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Learning many related tasks at the same time with backpropagation
Rich Caruana · 1995
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Discovering Neural Nets with Low Kolmogorov Complexity and High Generalization Capability
Jürgen Schmidhuber · 1997
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Reinforcement Learning with Self-Modifying Policies
Jürgen Schmidhuber, Jieyu Zhao, and Nicol N. Schraudolph · 1998
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Learning to forget: Continual prediction with LSTM
Felix A. Gers, Jurgen Schmidhuber, and Fred Cummins · 1999
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Artificial curiosity based on discovering novel algorithmic predictability through coevolution
Jürgen Schmidhuber · 1999
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Gradient-based optimization of hyperparameters
Yoshua Bengio · 2000
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Meta-learning with backpropagation
Steven Younger, Sepp Hochreiter, and Peter Conwell · 2001
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A taxonomy of global optimization methods based on response surfaces
Donald R. Jones · 2001
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Evolving neural networks through augmenting topologies
Kenneth O. Stanley and Risto Miikkulainen · 2002
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A perspective view and survey of meta-learning
R Vilalta and Y Drissi · 2002
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Many-layered learning
P. E. Utgoff and D. J. Stracuzzi · 2002
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Meta-learning in reinforcement learning
Nicolas Schweighofer and Kenji Doya · 2003
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Goedel Machines: Self-Referential Universal Problem Solvers Making Provably Optimal Self-Improvements
Jürgen Schmidhuber · 2003
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A meta-learning system based on genetic algorithms
Eric Pellerin, Luc Pigeon, and Sylvain Delisle · 2004
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Evolving soccer keepaway players through task decomposition
Shimon Whiteson, Nate Kohl, Risto Miikkulainen, and Peter Stone · 2005
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Ranking and selecting clustering algorithms using a meta-learning approach
Marcilio C.P. De Souto, Ricardo B.C. Prudêncio, Rodrigo G.F. Soares, Daniel S.A. De Araujo, Ivan G. Costa, Teresa B. Ludermir, and Alexander Schliep · 2008
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2010
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Meta-learning approach to neural network optimization
Pavel Kordík, Jan Koutník, Jan Drchal, Oleg Kovářík, Miroslav Čepek, and Miroslav Šnorek · 2010
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Meta-learning for time series forecasting and forecast combination
Christiane Lemke and Bogdan Gabrys · 2010
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Caltech-ucsd Birds 200
Peter Welinder, Steve Branson, Takeshi Mita, Catherine Wah, and Florian Schroff · 2010
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Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2010
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Algorithms for hyper-parameter optimization
James Bergstra, Remi Bardenet, Yoshua Bengio, and Balazs Kegl · 2011
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Deep Learning of Representations for Unsupervised and Transfer Learning, 2011
Yoshua Bengio · 2011
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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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Multi-objective optimization and Meta-learning for SVM parameter selection
Pericles B.C. Miranda, Ricardo B.C. Prudencio, Andre Carlos P.L.F. De Carvalho, and Carlos Soares · 2012
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Random search for hyper-parameter optimization
James Bergstra and Yoshua Bengio · 2012
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ADADELTA: An Adaptive Learning Rate Method
Matthew D. Zeiler · 2012
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Deterministic policy gradient algorithms
David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin Riedmiller · 2014
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On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models
Jürgen Schmidhuber · 2015
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Siamese Thesis
Gregory Koch and Gregory Koch · 2015
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Gradient-based hyperparameter optimization through reversible learning
Dougal Maclaurin, David Duvenaud, and Ryan P. Adams · 2015
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A set of complexity measures designed for applying meta-learning to instance selection
Enrique Leyva, Antonio González, and Raúl Pérez · 2015
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Human-level concept learning through probabilistic program induction
Brenden M. Lake, Ruslan Salakhutdinov, and Joshua B. Tenenbaum · 2015
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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, Alexander C. Berg, and Li Fei-Fei · 2015
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Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves
Tobias Domhan, Jost Tobias Springenberg, and Frank Hutter · 2015
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Efficient and robust automated machine learning
Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Tobias Springenberg, Manuel Blum, and Frank Hutter · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Lei Ba · 2015
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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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Attend, infer, repeat: Fast scene understanding with generative models
S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, Koray Kavukcuoglu, and Geoffrey E. Hinton · 2016
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Learning to learn neural networks
Tom Bosc · 2016
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Meta-Learning with Memory-Augmented Neural Networks
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lillicrap · 2016
Cited alongside, same era.
RLˆ2: Fast Reinforcement Learning via Slow Reinforcement Learning
Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever, and Pieter Abbeel · 2016
Cited alongside, same era.
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
Cited alongside, same era.
InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
Cited alongside, same era.
Towards a neural statistician
Harrison Edwards and Amos Storkey · 2016
Cited alongside, same era.
Wide Residual Networks
Sergey Zagoruyko and Nikos Komodakis · 2016
Probabilistic model-agnostic meta-learning
Chelsea Finn, Kelvin Xu, and Sergey Levine · 2018
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The importance of sampling in meta-reinforcement learning
Bradly Stadie, Ge Yang, Rein Houthooft, Xi Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, and Hya Sutskever · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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TACO: Learning task decomposition via temporal alignment for control
Kyriacos Shiarlis, Markus Wulfmeier, Sasha Salter, Shimon Whiteson, and Ingmar Posner · 2018
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One-shot high-fidelity imitation: Training large-scale deep nets with RL
Tom Le Paine, Sergio Gómez Colmenarejo, Ziyu Wang, Scott Reed, Yusuf Aytar, Tobias Pfaff, Matt W. Hoffman, Gabriel Barth-Maron, Serkan Cabi, David Budden, and Nando de Freitas · 2018
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Cited alongside, same era.
Stein variational gradient descent: A general purpose Bayesian inference algorithm
Qiang Liu and Dilin Wang · 2016
Cited alongside, same era.
OpenAI gym
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
Cited alongside, same era.
Continuous Control with Deep Reinforcement Learning
Timothy Lillicrap, Jonathan Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2016
Cited alongside, same era.
Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 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.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
Cited alongside, same era.
Neil C. Rabinowitz, Frank Perbet, H. Francis Song, Chiyuan Zhang, and Matthew Botvinick · 2018
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AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence
Jeff Clune · 2019
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Paired open-ended trailblazer (POET): Endlessly generating increasingly complex and diverse learning environments and their solutions
Rui Wang, Joel Lehman, Jeff Clune, and Kenneth O. Stanley · 2019
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Automated machine learning
Frank Hutter · 2019
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Few-shot Learning: A Survey
Yaqing Wang and Quanming Yao · 2019
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Meta reinforcement learning as task inference
Jan Humplik, Alexandre Galashov, Leonard Hasenclever, Pedro A. Ortega, Yee Whye Teh, and Nicolas Heess · 2019
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Efficient off-policy meta-reinforcement learning via probabilistic context variables
Kate Rakelly, Aurick Zhou, Deirdre Quiilen, Chelsea Finn, and Sergey Levine · 2019
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Causal Reasoning from Meta-reinforcement Learning
Ishita Dasgupta, Jane Wang, Silvia Chiappa, Jovana Mitrovic, Pedro Ortega, David Raposo, Edward Hughes, Peter Battaglia, Matthew Botvinick, and Zeb Kurth-Nelson · 2019
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Learning to adapt in dynamic, real-world environments through meta-reinforcement learning
Anusha Nagabandi, Ignasi Clavera, Simin Liu, Ronald S. Fearing, Pieter Abbeel, Sergey Levine, and Chelsea Finn · 2019
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Meta-learning: from few-shot learning to rapid reinforcement learning
Chelsea Finn · 2019
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Watch, Try, Learn: Meta-Learning from Demonstrations and Reward
Allan Zhou, Eric Jang, Daniel Kappler, Alex Herzog, Mohi Khansari, Paul Wohlhart, Yunfei Bai, Mrinal Kalakrishnan, Sergey Levine, and Chelsea Finn · 2019
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Deep online learning via meta-learning: Continual adaptation for model-based RL
Anusha Nagabandi, Chelsea Finn, and Sergey Levine · 2019
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Stochastic latent actor-critic: Deep reinforcement learning with a latent variable model
Alex X. Lee, Anusha Nagabandi, Pieter Abbeel, and Sergey Levine · 2019
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Unsupervised learning via meta-learning
Kyle Hsu, Sergey Levine, and Chelsea Finn · 2019
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Meta-learning update rules for unsupervised representation learning
Luke Metz, Jascha Sohl-Dickstein, Niru Maheswaranathan, and Brian Cheung · 2019
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Understanding and improving interpolation in autoencoders via an adversarial regularizer
David Berthelot, Ian Goodfellow, Colin Raffel, and Aurko Roy · 2019
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Online Meta-Learning
Chelsea Finn, Aravind Rajeswaran, Sham Kakade, and Sergey Levine · 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, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, and Hugo Larochelle · 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 differentiable closed-form solvers
Luca Bertinetto, Philip H.S. Torr, João Henriques, and Andrea Vedaldi · 2019
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Learning to remember rare events
Lukasz Kaiser, Aurko Roy, Ofir Nachum, and Samy Bengio · 2019
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Learning to propagate labels: Transductive propagation network for few-shot learning
Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, Eunho Yang, Sung Ju Hwang, and Yi Yang · 2019
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Meta-learning with latent embedding optimization
Andrei A. Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, and Raia Hadsell · 2019
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Adaptive Cross-Modal Few-Shot Learning
Chen Xing, Negar Rostamzadeh, Boris N. Oreshkin, and Pedro O. Pinheiro · 2019
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Differentially Private Meta-Learning
Jeffrey Li, Mikhail Khodak, Sebastian Caldas, and Ameet Talwalkar · 2019
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On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms
Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar · 2019
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Taming MAML: Efficient unbiased meta-reinforcement learning
Hao Liu, Richard Socher, and Caiming Xiong · 2019
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ES-MAML: Simple Hessian-Free Meta Learning
Xingyou Song, Wenbo Gao, Yuxiang Yang, Krzysztof Choromanski, Aldo Pacchiano, and Yunhao Tang · 2019
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PrOMP: Proximal meta-policy search
Jonas Rothfuss, Tamim Asfour, Dennis Lee, Ignasi Clavera, and Pieter Abbeel · 2019
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How to train your MAML
Antreas Antoniou, Amos Storkey, and Harrison Edwards · 2019
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MetaPix: Few-Shot Video Retargeting
Jessica Lee, Deva Ramanan, and Rohit Girdhar · 2019
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Improving Generalization in Meta Reinforcement Learning using Learned Objectives
Louis Kirsch, Sjoerd van Steenkiste, and Jürgen Schmidhuber · 2019
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Meta-Amortized Variational Inference and Learning
Mike Wu, Kristy Choi, Noah Goodman, and Stefano Ermon · 2019
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Few-Shot Learning as Domain Adaptation: Algorithm and Analysis
Jiechao Guan, Zhiwu Lu, Tao Xiang, and Ji-Rong Wen · 2020
Closest in time.
AutoML-Zero: Evolving Machine Learning Algorithms From Scratch
Esteban Real, Chen Liang, David R. So, and Quoc V. Le · 2020
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Boosting Few-Shot Learning With Adaptive Margin Loss
Aoxue Li, Weiran Huang, Xu Lan, Jiashi Feng, Zhenguo Li, and Liwei Wang · 2020
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A Two-Stage Approach to Few-Shot Learning for Image Recognition
Debasmit Das and C. S.George Lee · 2020
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Learning to Learn Cropping Models for Different Aspect Ratio Requirements
Debang Li, Junge Zhang, and Kaiqi Huang · 2020
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Learning Meta Face Recognition in Unseen Domains
Jianzhu Guo, Xiangyu Zhu, Chenxu Zhao, Dong Cao, Zhen Lei, and Stan Z. Li · 2020
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Scene-Adaptive Video Frame Interpolation via Meta-Learning
Myungsub Choi, Janghoon Choi, Sungyong Baik, Tae Hyun Kim, and Kyoung Mu Lee · 2020
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Meta-Q-Learning
Rasool Fakoor, Pratik Chaudhari, Stefano Soatto, and Alexander Smola · 2020
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Meta-learning curiosity algorithms
Ferran Alet, Martin F. Schneider, Tomas Lozano-Perez, and Leslie Pack Kaelbling · 2020
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Meta Reinforcement Learning with Autonomous Inference of Subtask Dependencies
Sungryull Sohn, Hyunjae Woo, Jongwook Choi, and Honglak Lee · 2020
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