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Drawing inspiration from gradient-based meta-learning methods with infinitely small gradient steps, we introduce Continuous-Time Meta-Learning (COMLN), a meta-learning algorithm where adaptation follows the dynamics of a gradient vector field.
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Meta-Learning Representations for Continual Learning
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Meta-learning with Differentiable Convex Optimization
Kwonjoon Lee, Subhransu Maji, Avinash Ravichandran, and Stefano Soatto · 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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Shadowing Properties of Optimization Algorithms
Antonio Orvieto and Aurelien Lucchi · 2019
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Rapid learning or feature reuse? towards understanding the effectiveness of maml
Aniruddh Raghu, Maithra Raghu, Samy Bengio, and Oriol Vinyals · 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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Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
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Forward and reverse gradient-based hyperparameter optimization
Luca Franceschi, Michele Donini, Paolo Frasconi, and Massimiliano Pontil · 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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Prototypical Networks for Few-shot Learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Meta-learning with Differentiable Closed-Form Solvers
Luca Bertinetto, Joao F Henriques, Philip HS Torr, and Andrea Vedaldi · 2018
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ProMP: Proximal Meta-Policy Search
Jonas Rothfuss, Dennis Lee, Ignasi Clavera, Tamim Asfour, and Pieter Abbeel · 2019
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Truncated Back-propagation for Bilevel Optimization
Amirreza Shaban, Ching-An Cheng, Nathan Hatch, and Byron Boots · 2019
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Fast context adaptation via meta-learning
Luisa Zintgraf, Kyriacos Shiarli, Vitaly Kurin, Katja Hofmann, and Shimon Whiteson · 2019
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Meta-Learning with Warped Gradient Descent
Sebastian Flennerhag, Andrei A Rusu, Razvan Pascanu, Francesco Visin, Hujun Yin, and Raia Hadsell · 2020
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Haiku: Sonnet for JAX, 2020
Tom Hennigan, Trevor Cai, Tamara Norman, and Igor Babuschkin · 2020
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Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?
Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B Tenenbaum, and Phillip Isola · 2020
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Gradient Surgery for Multi-Task Learning
Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn · 2020
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Task Attended Meta-Learning for Few-Shot Learning
Aroof Aimen, Sahil Sidheekh, and Narayanan C Krishnan · 2021
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Embedding Adaptation is Still Needed for Few-Shot Learning
Sébastien MR Arnold and Fei Sha · 2021
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Uniform Sampling over Episode Difficulty
Sébastien MR Arnold, Guneet S Dhillon, Avinash Ravichandran, and Stefano Soatto · 2021
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Personalized Algorithm Generation: A Case Study in Meta-Learning ODE Integrators
Yue Guo, Felix Dietrich, Tom Bertalan, Danimir T Doncevic, Manuel Dahmen, Ioannis G Kevrekidis, and Qianxiao Li · 2021
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Online hyperparameter optimization by Real-Time Recurrent Learning
Daniel Jiwoong Im, Cristina Savin, and Kyunghyun Cho · 2021
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Meta-Learning with Adjoint Methods
Shibo Li, Zheng Wang, Akil Narayan, Robert Kirby, and Shandian Zhe · 2021
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Gradient-based Hyperparameter Optimization Over Long Horizons
Paul Micaelli and Amos Storkey · 2021
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BOIL: Towards Representation Change for Few-Shot Learning
Jaehoon Oh, Hyungjun Yoo, ChangHwan Kim, and Se-Young Yun · 2021
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Learning where to learn: Gradient sparsity in meta and continual learning
Johannes Von Oswald, Dominic Zhao, Seijin Kobayashi, Simon Schug, Massimo Caccia, Nicolas Zucchet, and João Sacramento · 2021
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Meta Learning in the Continuous Time Limit
Ruitu Xu, Lin Chen, and Amin Karbasi · 2021
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MetaNODE: Prototype Optimization as a Neural ODE for Few-Shot Learning
Baoquan Zhang, Xutao Li, Yunming Ye, Shanshan Feng, and Rui Ye · 2021
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Meta-Learning with Neural Tangent Kernels
Yufan Zhou, Zhenyi Wang, Jiayi Xian, Changyou Chen, and Jinhui Xu · 2021
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