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The scope of the Baldwin effect was recently called into question by two papers that closely examined the seminal work of Hinton and Nowlan.
Asynchronous methods for deep reinforcement learning. In International Conference on Machine Learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. 2016 · 1937
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How learning can guide evolution
Geoffrey E Hinton and Steven J Nowlan. 1987 · 1987
Earlier work this paper cites.
Natural selection: when learning guides evolution
John Maynard Smith. 1987 · 1987
Earlier work this paper cites.
A comparative analysis of selection schemes used in genetic algorithms
David E Goldberg and Kalyanmoy Deb. 1991 · 1991
Earlier work this paper cites.
Evolution and learning in neural networks: the number and distribution of learning trials affect the rate of evolution. In Advances in Neural Information Processing Systems
Ron Keesing and David G Stork. 1991 · 1991
Earlier work this paper cites.
Maturation and the evolution of imitative learning in artificial organisms
Federico Cecconi, Filippo Menczer, and Richard K Belew. 1995 · 1995
Earlier work this paper cites.
Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm
Peter D Turney. 1995 · 1995
Earlier work this paper cites.
How adaptive antibodies facilitate the evolution of natural antibodies
Russell Wayne Anderson. 1996 · 1996
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Simple principles of metalearning
Juergen Schmidhuber, Jieyu Zhao, and MA Wiering. 1996 · 1996
Earlier work this paper cites.
Learning to learn: Introduction and overview
Sebastian Thrun and Lorien Pratt. 1998 · 1998
Earlier work this paper cites.
Fast learning for problem classes using knowledge based network initialization. In Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Michael Husken and Christian Goerick. 2000 · 2000
Earlier work this paper cites.
The evolution of variable learning rates. In Proceedings of the 4th Annual Conference on Genetic and Evolutionary Computation
John A Bullinaria. 2002 · 2002
Cited alongside, same era.
Myths and legends of the Baldwin effect
Peter D Turney. 2002 · 2002
Cited alongside, same era.
Lamarckian evolution and the Baldwin effect in evolutionary neural networks
PA Castillo, MG Arenas, JG Castellano, JJ Merelo, A Prieto, V Rivas, , and G Romero. 2006 · 2006
Cited alongside, same era.
Natural Evolution Strategies. In Proceedings of the Congress on Evolutionary Computation (CEC08), Hongkong
Daan Wierstra, Tom Schaul, Jan Peters, and Jürgen Schmidhuber. 2008 · 2008
Cited alongside, same era.
The Baldwin effect in developing neural networks. In Proceedings of the 12th annual conference on Genetic and evolutionary computation
Keith L Downing. 2010 · 2010
Cited alongside, same era.
Phenotypic plasticity, the baldwin effect, and the speeding up of evolution: The computational roots of an illusion
Mauro Santos, Eörs Szathmáry, and José F Fontanari. 2015 · 2015
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The Evolution of Sex through the Baldwin Effect
Larry Bull. 2016 · 2016
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RL2: Fast Reinforcement Learning via Slow Reinforcement Learning
Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever, and Pieter Abbeel. 2016 · 2016
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Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle. 2016 · 2016
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One-shot learning with memory-augmented neural networks
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lillicrap. 2016 · 2016
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One shot learning of simple visual concepts. In Proceedings of the Annual Meeting of the Cognitive Science Society
Brenden Lake, Ruslan Salakhutdinov, Jason Gross, and Joshua Tenenbaum. 2011 · 2011
Cited alongside, same era.
High Dimensions and Heavy Tails for Natural Evolution Strategies. In Genetic and Evolutionary Computation Conference (GECCO)
Tom Schaul, Tobias Glasmachers, and Jürgen Schmidhuber. 2011 · 2011
Cited alongside, same era.
MuJoCo: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa. 2012 · 2012
Cited alongside, same era.
Evolution in four dimensions, revised edition: Genetic, epigenetic, behavioral, and symbolic variation in the history of life
Eva Jablonka and Marion J Lamb. 2014 · 2014
Cited alongside, same era.
Natural Evolution Strategies
Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jan Peters, and Jürgen Schmidhuber. 2014 · 2014
Cited alongside, same era.
Siamese neural networks for one-shot image recognition. In ICML Deep Learning Workshop
Gregory Koch, Richard Zemel, and Ruslan Salakhutdinov. 2015 · 2015
Cited alongside, same era.
A new factor in evolution
J Mark Baldwin. 1896
Cited in the paper.
Later among the works it cites.
Matching networks for one shot learning. In Advances in Neural Information Processing Systems
Oriol Vinyals, Charles Blundell, Tim Lillicrap, Daan Wierstra, et al · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017 · 2017
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The revival of the Baldwin effect
José F Fontanari and Mauro Santos. 2017 · 2017
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Population Based Training of Neural Networks
Max Jaderberg, Valentin Dalibard, Simon Osindero, Wojciech M Czarnecki, Jeff Donahue, Ali Razavi, Oriol Vinyals, Tim Green, Iain Dunning, Karen Simonyan, et al · 2017
Later among the works it cites.
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. 2017 · 2017
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