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The search for neural architecture is producing many of the most exciting results in artificial intelligence.
Random search and reproducibility for neural architecture search
Liam Li and Ameet Talwalkar · 1902
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Evaluating the search phase of neural architecture search
Christian Sciuto, Kaicheng Yu, Martin Jaggi, Claudiu Musat, and Mathieu Salzmann · 1902
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Deconstructing lottery tickets: Zeros, signs, and the supermask
Hattie Zhou, Janice Lan, Rosanne Liu, and Jason Yosinski · 1905
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Winning the lottery with continuous sparsification
Pedro Savarese, Hugo Silva, and Michael Maire · 1912
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Single-cell responses in striate cortex oof kittens deprived of vision in one eye
Torsten N. Wiesel and David H. Hubel · 1963
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Reinforcement learning using neural networks, with applications to motor control
Rémi Coulom · 2002
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Evolving neural networks through augmenting topologies
Kenneth O. Stanley and Risto Miikkulainen · 2002
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Structural plasticity: Rewiring the brain
Heidi Johansen-Berg · 2006
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Experience-dependent structural synaptic plasticity in the mammalian brain
Anthony Holtmaat and Karel Svoboda · 2009
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A hypercube-based encoding for evolving large-scale neural networks
Kenneth O Stanley, David B D’Ambrosio, and Jason Gauci · 2009
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Enhancing es-hyperneat to evolve more complex regular neural networks
Sebastian Risi and Kenneth O Stanley · 2011
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Brain structural and functional development: genetics and experience
Nicoletta Berardi, Alessandro Sale, and Lamberto Maffei · 2015
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High-dimensional continuous control using generalized advantage estimation
John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel · 2015
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David Ha, Andrew M. Dai, and Quoc V. Le · 2016
Cited alongside, same era.
The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2018
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Alleviating catastrophic forgetting using context-dependent gating and synaptic stabilization
Nicolas Y Masse, Gregory D Grant, and David J Freedman · 2018
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Network pruning via transformable architecture search, 2019
Xuanyi Dong and Yi Yang · 2019
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Neural architecture search: A survey, 2019
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2019
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Weight agnostic neural networks
Adam Gaier and David Ha · 2019
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Evolving artificial neural networks with feedback
Sebastian Herzog, Christian Tetzlaff, and Florentin Wörgötter · 2019
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil C. Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell · 2016
Cited alongside, same era.
Openai baselines
Prafulla Dhariwal, Christopher Hesse, Oleg Klimov, Alex Nichol, Matthias Plappert, Alec Radford, John Schulman, Szymon Sidor, Yuhuai Wu, and Peter Zhokhov · 2017
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Evolution strategies as a scalable alternative to reinforcement learning, 2017
Tim Salimans, Jonathan Ho, Xi Chen, Szymon Sidor, and Ilya Sutskever · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Cited alongside, same era.
Neural architecture search with reinforcement learning, 2017
Barret Zoph and Quoc V. Le · 2017
Cited alongside, same era.
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Soft threshold weight reparameterization for learnable sparsity, 2020
Aditya Kusupati, Vivek Ramanujan, Raghav Somani, Mitchell Wortsman, Prateek Jain, Sham Kakade, and Ali Farhadi · 2020
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Brain structural plasticity: From adult neurogenesis to immature neurons
Chiara La Rosa, Roberta Parolisi, and Luca Bonfanti · 2020
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Geometry-aware gradient algorithms for neural architecture search, 2020
Liam Li, Mikhail Khodak, Maria-Florina Balcan, and Ameet Talwalkar · 2020
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Random mutation search
Samuel Schmidgall · 2021
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