Fetching the paper…
Reading the bibliography…
A core capability of intelligent systems is the ability to quickly learn new tasks by drawing on prior experience.
The complexity of partial derivatives
Walter Baur and Volker Strassen · 1983
Earlier work this paper cites.
Evolutionary principles in self-referential learning
Jurgen Schmidhuber · 1987
Earlier work this paper cites.
Meta-neural networks that learn by learning
Devang K Naik and RJ Mammone · 1992
Earlier work this paper cites.
Some bounds on the complexity of gradients, jacobians, and hessians
Andreas Griewank · 1993
Earlier work this paper cites.
Learning to learn
Sebastian Thrun and Lorien Pratt · 1998
Earlier work this paper cites.
Numerical optimization (springer series in operations research and financial engineering)
Jorge Nocedal and Stephen J. Wright · 2000
Earlier work this paper cites.
Learning to learn using gradient descent
Sepp Hochreiter, A Steven Younger, and Peter R Conwell · 2001
Earlier work this paper cites.
Enabling user-driven checkpointing strategies in reverse-mode automatic differentiation
Laurent Hascoët and Mauricio Araya-Polo · 2006
Earlier work this paper cites.
Cubic regularization of newton method and its global performance
Yurii Nesterov and Boris T. Polyak · 2006
Earlier work this paper cites.
Efficient multiple hyperparameter learning for log-linear models
Chuong B. Do, Chuan-Sheng Foo, and Andrew Y. Ng · 2007
Earlier work this paper cites.
Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation
Andreas Griewank and Andrea Walther · 2008
Earlier work this paper cites.
Deep learning via hessian-free optimization
James Martens · 2010
Earlier work this paper cites.
One shot learning of simple visual concepts
Brenden M Lake, Ruslan Salakhutdinov, Jason Gross, and Joshua B Tenenbaum · 2011
Earlier work this paper cites.
Generic methods for optimization-based modeling
Justin Domke · 2012
Earlier work this paper cites.
Automatic differentiation in machine learning: a survey
Atilim Gunes Baydin, Barak A. Pearlmutter, and Alexey Radul · 2015
Earlier work this paper cites.
Convex optimization: Algorithms and complexity
Sebastien Bubeck · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Siamese neural networks for one-shot image recognition
Gregory Koch · 2015
Earlier work this paper cites.
Gradient-based hyperparameter optimization through reversible learning
Dougal Maclaurin, David Duvenaud, and Ryan Adams · 2015
Earlier work this paper cites.
Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael I Jordan, and Philipp Moritz · 2015
Earlier work this paper cites.
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
Cited alongside, same era.
Rl2: Fast reinforcement learning via slow reinforcement learning
Yan Duan, John Schulman, Xi Chen, Peter L Bartlett, Ilya Sutskever, and Pieter Abbeel · 2016
Cited alongside, same era.
Ke Li and Jitendra Malik · 2016
Cited alongside, same era.
Hyperparameter optimization with approximate gradient
Fabian Pedregosa · 2016
Cited alongside, same era.
Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2016
Cited alongside, same era.
Ferran Alet, Tomás Lozano-Pérez, and Leslie P Kaelbling · 2018
Later among the works it cites.
Meta-learning with differentiable closed-form solvers
Luca Bertinetto, Joao F Henriques, Philip HS Torr, and Andrea Vedaldi · 2018
Later among the works it cites.
Learning to Learn with Gradients
Chelsea Finn · 2018
Later among the works it cites.
Probabilistic model-agnostic meta-learning
Chelsea Finn, Kelvin Xu, and Sergey Levine · 2018
Later among the works it cites.
Recasting gradient-based meta-learning as hierarchical bayes
Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, and Thomas Griffiths · 2018
Later among the works it cites.
Meta-learning priors for efficient online bayesian regression
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Meta-learning with memory-augmented neural networks
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lillicrap · 2016
Cited alongside, same era.
Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Tim Lillicrap, Daan Wierstra, et al · 2016
Cited alongside, same era.
Learning to reinforcement learn
Jane X Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z Leibo, Remi Munos, Charles Blundell, Dharshan Kumaran, and Matt Botvinick · 2016
Cited alongside, same era.
Continuous adaptation via meta-learning in nonstationary and competitive environments
Maruan Al-Shedivat, Trapit Bansal, Yuri Burda, Ilya Sutskever, Igor Mordatch, and Pieter Abbeel · 2017
Cited alongside, same era.
Optnet: Differentiable optimization as a layer in neural networks
Brandon Amos and J Zico Kolter · 2017
Cited alongside, same era.
Chelsea Finn and Sergey Levine · 2017
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Cited alongside, same era.
James Harrison, Apoorva Sharma, and Marco Pavone · 2018
Later among the works it cites.
Auto-meta: Automated gradient based meta learner search
Jaehong Kim, Youngduck Choi, Moonsu Cha, Jung Kwon Lee, Sangyeul Lee, Sungwan Kim, Yongseok Choi, and Jiwon Kim · 2018
Later among the works it cites.
Concept learning with energy-based models
Igor Mordatch · 2018
Later among the works it cites.
On first-order meta-learning algorithms
Alex Nichol, Joshua Achiam, and John Schulman · 2018
Later among the works it cites.
Tadam: Task dependent adaptive metric for improved few-shot learning
Boris Oreshkin, Pau Rodríguez López, and Alexandre Lacoste · 2018
Later among the works it cites.
Meta-learning with latent embedding optimization
Andrei A Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, and Raia Hadsell · 2018
Later among the works it cites.
Truncated back-propagation for bilevel optimization
Amirreza Shaban, Ching-An Cheng, Olivia Hirschey, and Byron Boots · 2018
Later among the works it cites.
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
Later among the works it cites.
Infinite mixture prototypes for few-shot learning
Kelsey R Allen, Evan Shelhamer, Hanul Shin, and Joshua B Tenenbaum · 2019
Closest in time.
Online meta-learning
Chelsea Finn, Aravind Rajeswaran, Sham Kakade, and Sergey Levine · 2019
Closest in time.
A differentiable augmented lagrangian method for bilevel nonlinear optimization
Benoit Landry, Zachary Manchester, and Marco Pavone · 2019
Closest in time.
Meta-learning with differentiable convex optimization
Kwonjoon Lee, Subhransu Maji, Avinash Ravichandran, and Stefano Soatto · 2019
Closest in time.
Meta-learning for low-resource natural language generation in task-oriented dialogue systems
Fei Mi, Minlie Huang, Jiyong Zhang, and Boi Faltings · 2019
Closest in time.
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.