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Learning quickly is of great importance for machine intelligence deployed in online platforms.
Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al · 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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Pathnet: Evolution channels gradient descent in super neural networks
Chrisantha Fernando, Dylan Banarse, Charles Blundell, Yori Zwols, David Ha, Andrei A Rusu, Alexander Pritzel, and Daan Wierstra · 2017
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Chelsea Finn and Sergey Levine · 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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Few-shot learning with graph neural networks
Victor Garcia and Joan Bruna · 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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Lifelong learning with dynamically expandable networks
Jeongtae Lee, Jaehong Yun, Sungju Hwang, and Eunho Yang · 2017
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Meta-sgd: Learning to learn quickly for few shot learning
Zhenguo Li, Fengwei Zhou, Fei Chen, and Hang Li · 2017
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Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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A simple neural attentive meta-learner
Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel · 2017
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icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert · 2017
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Continual learning with deep generative replay
Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
Cited alongside, same era.
Modular meta-learning
Ferran Alet, Tomas Lozano-Perez, and Leslie P Kaelbling · 2018
Cited alongside, same era.
Riemannian walk for incremental learning: Understanding forgetting and intransigence
Arslan Chaudhry, Puneet K Dokania, Thalaiyasingam Ajanthan, and Philip HS Torr · 2018
Cited alongside, same era.
Probabilistic model-agnostic meta-learning
Chelsea Finn, Kelvin Xu, and Sergey Levine · 2018
Cited alongside, same era.
Recasting gradient-based meta-learning as hierarchical bayes
Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, and Thomas Griffiths · 2018
Cited alongside, same era.
Continual learning with adaptive weights (claw)
Tameem Adel, Han Zhao, and Richard E Turner · 2019
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Efficient lifelong learning with a-GEM
Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny · 2019
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Online meta-learning
Chelsea Finn, Aravind Rajeswaran, Sham Kakade, and Sergey Levine · 2019
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Meta-learning with warped gradient descent
Sebastian Flennerhag, Andrei A Rusu, Razvan Pascanu, Hujun Yin, and Raia Hadsell · 2019
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Reconciling meta-learning and continual learning with online mixtures of tasks
Ghassen Jerfel, Erin Grant, Tom Griffiths, and Katherine A Heller · 2019
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Meta-learning for low-resource neural machine translation
Jiatao Gu, Yong Wang, Yun Chen, Victor OK Li, and Kyunghyun Cho · 2018
Cited alongside, same era.
Gradient-based meta-learning with learned layerwise metric and subspace
Yoonho Lee and Seungjin Choi · 2018
Cited alongside, same era.
Darts: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2018
Cited alongside, same era.
Transductive propagation network for few-shot learning
Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, and Yi Yang · 2018
Cited alongside, same era.
Deep online learning via meta-learning: Continual adaptation for model-based rl
Anusha Nagabandi, Chelsea Finn, and Sergey Levine · 2018
Cited alongside, same era.
Reptile: a scalable metalearning algorithm
Alex Nichol and John Schulman · 2018
Cited alongside, same era.
Tadam: Task dependent adaptive metric for improved few-shot learning
Boris Oreshkin, Pau Rodríguez López, and Alexandre Lacoste · 2018
Cited alongside, same era.
Xilai Li, Yingbo Zhou, Tianfu Wu, Richard Socher, and Caiming Xiong · 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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Out-of-domain detection for low-resource text classification tasks
Ming Tan, Yang Yu, Haoyu Wang, Dakuo Wang, Saloni Potdar, Shiyu Chang, and Mo Yu · 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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Multimodal model-agnostic meta-learning via task-aware modulation
Risto Vuorio, Shao-Hua Sun, Hexiang Hu, and Joseph J Lim · 2019
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Hierarchically structured meta-learning
Huaxiu Yao, Ying Wei, Junzhou Huang, and Zhenhui Li · 2019
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Tapnet: Neural network augmented with task-adaptive projection for few-shot learning
Sung Whan Yoon, Jun Seo, and Jaekyun Moon · 2019
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Frustratingly simple few-shot object detection
Xin Wang, Thomas E Huang, Trevor Darrell, Joseph E Gonzalez, and Fisher Yu · 2020
Closest in time.
Automated relational meta-learning
Huaxiu Yao, Xian Wu, Zhiqiang Tao, Yaliang Li, Bolin Ding, Ruirui Li, and Zhenhui Li · 2020
Closest in time.