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In recent years, by leveraging more data, computation, and diverse tasks, learned optimizers have achieved remarkable success in supervised learning, outperforming classical hand-designed optimizers.
Increased rates of convergence through learning rate adaptation
Robert A Jacobs · 1988
Earlier work this paper cites.
Adapting bias by gradient descent: An incremental version of delta-bar-delta
Richard S Sutton · 1992
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Recurrent neural networks
Larry R Medsker and LC Jain · 2001
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Using a thousand optimization tasks to learn hyperparameter search strategies
Luke Metz, Niru Maheswaranathan, Ruoxi Sun, C Daniel Freeman, Ben Poole, and Jascha Sohl-Dickstein · 2002
Earlier work this paper cites.
Luke Metz, Niru Maheswaranathan, C Daniel Freeman, Ben Poole, and Jascha Sohl-Dickstein · 2009
Earlier work this paper cites.
The MNIST database of handwritten digit images for machine learning research
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Tuning-free step-size adaptation
Ashique Rupam Mahmood, Richard S Sutton, Thomas Degris, and Patrick M Pilarski · 2012
Earlier work this paper cites.
Lecture 6.5-RMSProp: Divide the gradient by a running average of its recent magnitude
Tijmen Tieleman and Geoffrey Hinton · 2012
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On the properties of neural machine translation: Encoder–decoder approaches
Kyunghyun Cho, Bart van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio · 2014
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Error bounds for approximations with deep ReLU networks
Dmitry Yarotsky · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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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
Earlier work this paper cites.
Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
Earlier work this paper cites.
High-dimensional continuous control using generalized advantage estimation
John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel · 2016
Earlier work this paper cites.
Learning to learn without gradient descent by gradient descent
Yutian Chen, Matthew W Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Timothy P Lillicrap, Matt Botvinick, and Nando de Freitas · 2017
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Learning to optimize
Ke Li and Jitendra Malik · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Neural networks and rational functions
Matus Telgarsky · 2017
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Learned optimizers that scale and generalize
Olga Wichrowska, Niru Maheswaranathan, Matthew W Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Nando Freitas, and Jascha Sohl-Dickstein · 2017
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JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
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Where did my optimum go?: An empirical analysis of gradient descent optimization in policy gradient methods
Behaviour suite for reinforcement learning
Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvari, Satinder Singh, et al · 2020
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Meta-gradient reinforcement learning with an objective discovered online
Zhongwen Xu, Hado P van Hasselt, Matteo Hessel, Junhyuk Oh, Satinder Singh, and David Silver · 2020
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Meta learning via learned loss
Sarah Bechtle, Artem Molchanov, Yevgen Chebotar, Edward Grefenstette, Ludovic Righetti, Gaurav Sukhatme, and Franziska Meier · 2021
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Brax - a differentiable physics engine for large scale rigid body simulation, 2021
C. Daniel Freeman, Erik Frey, Anton Raichuk, Sertan Girgin, Igor Mordatch, and Olivier Bachem · 2021
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Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
Lu Lu, Pengzhan Jin, Guofei Pang, Zhongqiang Zhang, and George Em Karniadakis · 2021
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Peter Henderson, Joshua Romoff, and Joelle Pineau · 2018
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Evolved policy gradients
Rein Houthooft, Yuhua Chen, Phillip Isola, Bradly Stadie, Filip Wolski, OpenAI Jonathan Ho, and Pieter Abbeel · 2018
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Performance comparison of different momentum techniques on deep reinforcement learning
Mehmet Sarigül and Mutlu Avci · 2018
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Adafactor: Adaptive learning rates with sublinear memory cost
Noam Shazeer and Mitchell Stern · 2018
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Reinforcement Learning: An Introduction
Richard S Sutton and Andrew G Barto · 2018
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Deep reinforcement learning and the deadly triad
Hado Van Hasselt, Yotam Doron, Florian Strub, Matteo Hessel, Nicolas Sonnerat, and Joseph Modayil · 2018
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Correlation priors for reinforcement learning
Bastian Alt, Adrian Šošić, and Heinz Koeppl · 2019
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Understanding and preventing capacity loss in reinforcement learning
Clare Lyle, Mark Rowland, and Will Dabney · 2021
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Reverse engineering learned optimizers reveals known and novel mechanisms
Niru Maheswaranathan, David Sussillo, Luke Metz, Ruoxi Sun, and Jascha Sohl-Dickstein · 2021
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Unbiased gradient estimation in unrolled computation graphs with persistent evolution strategies
Paul Vicol, Luke Metz, and Jascha Sohl-Dickstein · 2021
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A closer look at learned optimization: Stability, robustness, and inductive biases
James Harrison, Luke Metz, and Jascha Sohl-Dickstein · 2022
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Towards continual reinforcement learning: A review and perspectives
Khimya Khetarpal, Matthew Riemer, Irina Rish, and Doina Precup · 2022
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Introducing symmetries to black box meta reinforcement learning
Louis Kirsch, Sebastian Flennerhag, Hado van Hasselt, Abram Friesen, Junhyuk Oh, and Yutian Chen · 2022
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Model-free policy learning with reward gradients
Qingfeng Lan, Samuele Tosatto, Homayoon Farrahi, and Rupam Mahmood · 2022
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Discovered policy optimisation
Chris Lu, Jakub Kuba, Alistair Letcher, Luke Metz, Christian Schroeder de Witt, and Jakob Foerster · 2022
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A state-of-the-art survey on solving non-iid data in federated learning
Xiaodong Ma, Jia Zhu, Zhihao Lin, Shanxuan Chen, and Yangjie Qin · 2022
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Discovering general reinforcement learning algorithms with adversarial environment design
Matthew Thomas Jackson, Minqi Jiang, Jack Parker-Holder, Risto Vuorio, Chris Lu, Gregory Farquhar, Shimon Whiteson, and Jakob Nicolaus Foerster · 2023
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Liyuan Wang, Xingxing Zhang, Hang Su, and Jun Zhu · 2023
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