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Model Agnostic Meta-Learning (MAML) has emerged as a standard framework for meta-learning, where a meta-model is learned with the ability of fast adapting to new tasks.
Evolutionary principles in self-referential learning
Jurgen Schmidhuber · 1987
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Generalized padé approximations to the exponential function
J. C. Butcher and F. H. Chipman · 1992
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The P a d e ´ Pad\acute{e} method for computing the matrix exponential
M.Arioli, B.Codenotti, and C.Fassino · 1996
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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Optimal transport – Old and new , volume 338, pp. xxii+973
C Villani · 2008
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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Alex Graves, Greg Wayne, and Ivo Danihelka · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gómez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando de Freitas · 2016
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Euclidean, Metric, and Wasserstein gradient flows: an overview, 2016
Filippo Santambrogio · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, and Daan Wierstra · 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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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard S. Zemel · 2017
Cited alongside, same era.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
Cited alongside, same era.
Probabilistic model-agnostic meta-learning
Chelsea Finn, Kelvin Xu, and Sergey Levine · 2018
Cited alongside, same era.
Countering adversarial images using input transformations
Chuan Guo, Mayank Rana, Moustapha Cisse, and Laurens van der Maaten · 2018
Cited alongside, same era.
Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 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.
Meta-learning: from few-shot learning to rapid reinforcement learning
Chelsea Finn and Sergey Levine · 2019
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Simple black-box adversarial attacks
Chuan Guo, Jacob R. Gardner, Yurong You, Andrew Gordon Wilson, and Kilian Q. Weinberger · 2019
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Wide neural networks of any depth evolve as linear models under gradient descent
Jaehoon Lee, Lechao Xiao, Samuel Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, and Jeffrey Pennington · 2019
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Bayesian deep convolutional networks with many channels are gaussian processes
Roman Novak, Lechao Xiao, Yasaman Bahri, Jaehoon Lee, Greg Yang, Jiri Hron, Daniel A. Abolafia, Jeffrey Pennington, and Jascha Sohl-dickstein · 2019
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Meta-learning with implicit gradients
Aravind Rajeswaran, Chelsea Finn, Sham Kakade, and Sergey Levine · 2019
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Amortized bayesian meta-learning
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On first-order meta-learning algorithms
Alex Nichol, Joshua Achiam, and John Schulman · 2018
Cited alongside, same era.
Tadam: Task dependent adaptive metric for improved few-shot learning
Boris N. Oreshkin, Pau Rodriguez, and Alexandre Lacoste · 2018
Cited alongside, same era.
Adversarial risk and the dangers of evaluating against weak attacks, 2018
Jonathan Uesato, Brendan O’Donoghue, Aaron van den Oord, and Pushmeet Kohli · 2018
Cited alongside, same era.
Bayesian model-agnostic meta-learning
Jaesik Yoon, Taesup Kim, Ousmane Dia, Sungwoong Kim, Yoshua Bengio, and Sungjin Ahn · 2018
Cited alongside, same era.
A convergence theory for deep learning via over-parameterization
Zeyuan Allen-Zhu, Yuanzhi Li, and Zhao Song · 2019
Cited alongside, same era.
On exact computation with an infinitely wide neural net
Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Ruslan Salakhutdinov, and Ruosong Wang · 2019
Cited alongside, same era.
Sachin Ravi and Alex Beatson · 2019
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Meta-learnt priors slow down catastrophic forgetting in neural networks
Giacomo Spigler · 2019
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Hierarchically structured meta-learning
Huaxiu Yao, Ying Wei, Junzhou Huang, and Zhenhui Li · 2019
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On the convergence theory of gradient-based model-agnostic meta-learning algorithms
Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar · 2020
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Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the neural tangent kernel
Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul, Sepideh Kharaghani, Daniel M. Roy, and Surya Ganguli · 2020
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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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Meta-dataset: A dataset of datasets for learning to learn from few examples
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, and Hugo Larochelle · 2020
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Provable meta-learning of linear representations
Nilesh Tripuraneni, Chi Jin, and Michael I. Jordan · 2020
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