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In order to efficiently learn with small amount of data on new tasks, meta-learning transfers knowledge learned from previous tasks to the new ones.
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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Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 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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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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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 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 and universality: Deep representations and gradient descent can approximate any learning algorithm
Chelsea Finn and Sergey Levine · 2018
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Probabilistic model-agnostic meta-learning
Chelsea Finn, Kelvin Xu, and Sergey Levine · 2018
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Recasting gradient-based meta-learning as hierarchical bayes
Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, and Thomas Griffiths · 2018
Cited alongside, same era.
Meta-learning for low-resource neural machine translation
Jiatao Gu, Yong Wang, Yun Chen, Kyunghyun Cho, and Victor OK Li · 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.
A simple neural attentive meta-learner
Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel · 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
Bayesian model-agnostic meta-learning
Jaesik Yoon, Taesup Kim, Ousmane Dia, Sungwoong Kim, Yoshua Bengio, and Sungjin Ahn · 2018
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Transferring knowledge across learning processes
Sebastian Flennerhag, Pablo G Moreno, Neil D Lawrence, and Andreas Damianou · 2019
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Meta-learning probabilistic inference for prediction
Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, and Richard E Turner · 2019
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Reconciling meta-learning and continual learning with online mixtures of tasks
Ghassen Jerfel, Erin Grant, Thomas L Griffiths, and Katherine Heller · 2019
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Few-shot object detection via feature reweighting
Bingyi Kang, Zhuang Liu, Xin Wang, Fisher Yu, Jiashi Feng, and Trevor Darrell · 2019
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Personalizing dialogue agents via meta-learning
Zhaojiang Lin, Andrea Madotto, Chien-Sheng Wu, and Pascale Fung · 2019
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Boris Oreshkin, Pau Rodríguez López, and Alexandre Lacoste · 2018
Cited alongside, same era.
Film: Visual reasoning with a general conditioning layer
Ethan Perez, Florian Strub, Harm de Vries, Vincent Dumoulin, and Aaron C. Courville · 2018
Cited alongside, same era.
Meta-learning for semi-supervised few-shot classification
Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, and Richard S. Zemel · 2018
Cited alongside, same era.
Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2018
Cited alongside, same era.
Toward multimodal model-agnostic meta-learning
Risto Vuorio, Shao-Hua Sun, Hexiang Hu, and Joseph J Lim · 2018
Cited alongside, same era.
Learning to compare: Relation network for few-shot learning
Flood Sung Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales · 2018
Cited alongside, same era.
Learning from multiple cities: A meta-learning approach for spatial-temporal prediction
Huaxiu Yao, Yiding Liu, Ying Wei, Xianfeng Tang, and Zhenhui Li
Cited in the paper.
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Few-shot unsupervised image-to-image translation
Ming-Yu Liu, Xun Huang, Arun Mallya, Tero Karras, Timo Aila, Jaakko Lehtinen, and Jan Kautz · 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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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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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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Metapred: Meta-learning for clinical risk prediction with limited patient electronic health records
Xi Sheryl Zhang, Fengyi Tang, Hiroko Dodge, Jiayu Zhou, and Fei Wang · 2019
Later among the works it cites.