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The goal of optimization-based meta-learning is to find a single initialization shared across a distribution of tasks to speed up the process of learning new tasks.
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Is learning the n-th thing any easier than learning the first?
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Learning to learn using gradient descent
Sepp Hochreiter, A Steven Younger, and Peter R Conwell · 2001
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A perspective view and survey of meta-learning
Ricardo Vilalta and Youssef Drissi · 2002
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Sobolev spaces
Robert A Adams and John JF Fournier · 2003
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Max-margin markov networks
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Large margin methods for structured and interdependent output variables
Ioannis Tsochantaridis, Thorsten Joachims, Thomas Hofmann, and Yasemin Altun · 2005
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Convexity, classification, and risk bounds
Peter L Bartlett, Michael I Jordan, and Jon D McAuliffe · 2006
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One-shot learning of object categories
Li Fei-Fei, Rob Fergus, and Pietro Perona · 2006
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Predicting structured data
Gökhan Bakir, Thomas Hofmann, Bernhard Schölkopf, Alexander J Smola, and Ben Taskar · 2007
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Optimal rates for the regularized least-squares algorithm
Andrea Caponnetto and Ernesto De Vito · 2007
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On early stopping in gradient descent learning
Yuan Yao, Lorenzo Rosasco, and Andrea Caponnetto · 2007
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A new approach to collaborative filtering: Operator estimation with spectral regularization
Jacob Abernethy, Francis Bach, Theodoros Evgeniou, and Jean-Philippe Vert · 2009
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Cifar 100 dataset
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton · 2009
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Hilbert space embeddings and metrics on probability measures
Bharath K Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Bernhard Schölkopf, and Gert RG Lanckriet · 2010
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Reproducing kernel Hilbert spaces in probability and statistics
Alain Berlinet and Christine Thomas-Agnan · 2011
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One shot learning of simple visual concepts
Brenden Lake, Ruslan Salakhutdinov, Jason Gross, and Joshua Tenenbaum · 2011
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Structured learning and prediction in computer vision
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A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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Multiclass learning with simplex coding
Youssef Mroueh, Tomaso Poggio, Lorenzo Rosasco, and Jean-Jeacques Slotine · 2012
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Understanding machine learning: From theory to algorithms
Shai Shalev-Shwartz and Shai Ben-David · 2014
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David Ha, Andrew Dai, and Quoc V Le · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Ke Li and Jitendra Malik · 2016
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Meta-learning with memory-augmented neural networks
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lillicrap · 2016
Few-shot image recognition by predicting parameters from activations
Siyuan Qiao, Chenxi Liu, Wei Shen, and Alan L Yuille · 2018
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Manifold structured prediction
Alessandro Rudi, Carlo Ciliberto, GianMaria Marconi, and Lorenzo Rosasco · 2018
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How to train your maml
Antreas Antoniou, Harrison Edwards, and Amos Storkey · 2019
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Meta-learning with differentiable closed-form solvers
Luca Bertinetto, Joao F Henriques, Philip HS Torr, and Andrea Vedaldi · 2019
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Localized structured prediction
Carlo Ciliberto, Francis Bach, and Alessandro Rudi · 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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Matching networks for one shot learning
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Low data drug discovery with one-shot learning
Han Altae-Tran, Bharath Ramsundar, Aneesh S Pappu, and Vijay Pande · 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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Meta-sgd: Learning to learn quickly for few-shot learning
Zhenguo Li, Fengwei Zhou, Fei Chen, and Hang Li · 2017
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Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2017
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Falkon: An optimal large scale kernel method
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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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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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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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Tafe-net: Task-aware feature embeddings for low shot learning
Xin Wang, Fisher Yu, Ruth Wang, Trevor Darrell, and Joseph E Gonzalez · 2019
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Hierarchically structured meta-learning
Huaxiu Yao, Ying Wei, Junzhou Huang, and Zhenhui Li · 2019
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Fast context adaptation via meta-learning
Luisa M Zintgraf, Kyriacos Shiarlis, Vitaly Kurin, Katja Hofmann, and Shimon Whiteson · 2019
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Diana Cai, Rishit Sheth, Lester Mackey, and Nicolo Fusi · 2020
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A general framework for consistent structured prediction with implicit loss embeddings
Carlo Ciliberto, Lorenzo Rosasco, and Alessandro Rudi · 2020
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The advantage of conditional meta-learning for biased regularization and fine-tuning
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Learning to balance: Bayesian meta-learning for imbalanced and out-of-distribution tasks
Hae Beom Lee, Hayeon Lee, Donghyun Na, Saehoon Kim, Minseop Park, Eunho Yang, and Sung Ju Hwang · 2020
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Kernel methods through the roof: handling billions of points efficiently
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Embedding propagation: Smoother manifold for few-shot classification
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