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An important research direction in machine learning has centered around developing meta-learning algorithms to tackle few-shot learning.
Siamese neural networks for one-shot image recognition
Gregory Koch, Richard Zemel, and Ruslan Salakhutdinov · 2015
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Convergent learning: Do different neural networks learn the same representations?
Yixuan Li, Jason Yosinski, Jeff Clune, Hod Lipson, and John E Hopcroft · 2015
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Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2016
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Meta-learning with memory-augmented neural networks
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lillicrap · 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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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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SVCCA: Singular vector canonical correlation analysis for deep learning dynamics and interpretability
Maithra Raghu, Justin Gilmer, Jason Yosinski, and Jascha Sohl-Dickstein · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Antreas Antoniou, Harrison Edwards, and Amos Storkey · 2018
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Identifying and controlling important neurons in neural machine translation
Anthony Bau, Yonatan Belinkov, Hassan Sajjad, Nadir Durrani, Fahim Dalvi, and James Glass · 2018
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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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Probabilistic model-agnostic meta-learning
Chelsea Finn, Kelvin Xu, and Sergey Levine · 2018
Cited alongside, same era.
Meta-Learning probabilistic inference for prediction
Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, and Richard E Turner · 2018
Cited alongside, same era.
A closer look at deep learning heuristics: Learning rate restarts, warmup and distillation
Akhilesh Gotmare, Nitish Shirish Keskar, Caiming Xiong, and Richard Socher · 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.
Meta-learning priors for efficient online bayesian regression
To what extent do different neural networks learn the same representation: A Neuron Activation Subspace Match Approach
Liwei Wang, Lunjia Hu, Jiayuan Gu, Zhiqiang Hu, Yue Wu, Kun He, and John E. Hopcroft · 2018
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Deep meta-learning: learning to learn in the concept space
Fengwei Zhou, Bin Wu, and Zhenguo Li · 2018
Later among the works it cites.
Fast context adaptation via meta-learning
Luisa M Zintgraf, Kyriacos Shiarlis, Vitaly Kurin, Katja Hofmann, and Shimon Whiteson · 2018
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Few-shot text classification with distributional signatures
Yujia Bao, Menghua Wu, Shiyu Chang, and Regina Barzilay · 2019
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James Harrison, Apoorva Sharma, and Marco Pavone · 2018
Cited alongside, same era.
Unsupervised learning via meta-learning
Kyle Hsu, Sergey Levine, and Chelsea Finn · 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.
Insights on representational similarity in neural networks with canonical correlation
Ari S Morcos, Maithra Raghu, and Samy Bengio · 2018
Cited alongside, same era.
Reptile: A scalable metalearning algorithm
Alex Nichol and John Schulman · 2018
Cited alongside, same era.
Meta-learning with latent embedding optimization
Andrei A Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, and Raia Hadsell · 2018
Cited alongside, same era.
Understanding learning dynamics of language models with SVCCA
Naomi Saphra and Adam Lopez · 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.
Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, and Jia-Bin Huang · 2019
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Meta-learning representations for continual learning
Khurram Javed and Martha White · 2019
Closest in time.
Similarity of neural network representations revisited
Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey Hinton · 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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Universality and individuality in neural dynamics across large populations of recurrent networks
Niru Maheswaranathan, Alex H. Willams, Matthew D. Golub, Surya Ganguli, and David Sussillo · 2019
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Transfusion: Understanding transfer learning with applications to medical imaging
Maithra Raghu, Chiyuan Zhang, Jon Kleinberg, and Samy Bengio · 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
Closest in time.