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Latest insights from biology show that intelligence not only emerges from the connections between neurons but that individual neurons shoulder more computational responsibility than previously anticipated.
Understanding the nature of the general factor of intelligence: the role of individual differences in neural plasticity as an explanatory mechanism
D Garlick · 2002
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin A. Riedmiller, Andreas Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
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Deep residual learning for image recognition
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Bridging the gaps between residual learning, recurrent neural networks and visual cortex
Qianli Liao and Tomaso A. Poggio · 2016
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Small-footprint deep neural networks with highway connections for speech recognition
Liang Lu and Steve Renals · 2016
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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Understanding and improving convolutional neural networks via concatenated rectified linear units
Wenling Shang, Kihyuk Sohn, Diogo Almeida, and Honglak Lee · 2016
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Deep reinforcement learning with double q-learning
Hado van Hasselt, Arthur Guez, and David Silver · 2016
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Residual networks behave like ensembles of relatively shallow networks
Andreas Veit, Michael J. Wilber, and Serge J. Belongie · 2016
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Generalized vandermonde tensors
Changqing Xu, Mingyue Wang, and Xian Li · 2016
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Openai gym
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2017
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Highway and residual networks learn unrolled iterative estimation
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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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Parametric exponential linear unit for deep convolutional neural networks
Ludovic Trottier, Philippe Giguère, and Brahim Chaib-draa · 2017
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V. Le · 2017
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Sigmoid-weighted linear units for neural network function approximation in reinforcement learning
Stefan Elfwing, Eiji Uchibe, and Kenji Doya · 2018
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IMPALA: scalable distributed deep-rl with importance weighted actor-learner architectures
Lasse Espeholt, Hubert Soyer, Rémi Munos, Karen Simonyan, Volodymyr Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, and Koray Kavukcuoglu · 2018
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Generalization and regularization in DQN
Jesse Farebrother, Marlos C. Machado, and Michael Bowling · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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Deep reinforcement learning that matters
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 2018
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Rainbow: Combining improvements in deep reinforcement learning
Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Gheshlaghi Azar, and David Silver · 2018
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Progressive neural architecture search
Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan L. Yuille, Jonathan Huang, and Kevin Murphy · 2018
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Learning combinations of activation functions
Franco Manessi and Alessandro Rozza · 2018
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Quantifying generalization in reinforcement learning
Karl Cobbe, Oleg Klimov, Christopher Hesse, Taehoon Kim, and John Schulman · 2019
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An evolution strategy with progressive episode lengths for playing games
Improved soft actor-critic: Mixing prioritized off-policy samples with on-policy experience
Chayan Banerjee, Zhiyong Chen, and Nasimul Noman · 2021
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Continual backprop: Stochastic gradient descent with persistent randomness
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Mastering atari with discrete world models
Danijar Hafner, Timothy P. Lillicrap, Mohammad Norouzi, and Jimmy Ba · 2021
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Counterfactual credit assignment in model-free reinforcement learning
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Johan S. Obando-Ceron and Pablo Samuel Castro · 2021
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Lior Fuks, Noor H. Awad, Frank Hutter, and Marius Thomas Lindauer · 2019
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An evaluation of parametric activation functions for deep learning
Luke B. Godfrey · 2019
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Learning activation functions: A new paradigm of understanding neural networks
Mohit Goyal, Rajan Goyal, and Brejesh Lall · 2019
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Recurrent experience replay in distributed reinforcement learning
Steven Kapturowski, Georg Ostrovski, John Quan, Rémi Munos, and Will Dabney · 2019
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SNAS: stochastic neural architecture search
Sirui Xie, Hehui Zheng, Chunxiao Liu, and Liang Lin · 2019
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Never give up: Learning directed exploration strategies
Adrià Puigdomènech Badia, Pablo Sprechmann, Alex Vitvitskyi, Zhaohan Daniel Guo, Bilal Piot, Steven Kapturowski, Olivier Tieleman, Martín Arjovsky, Alexander Pritzel, Andrew Bolt, and Charles Blundell · 2020
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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RL-DARTS: differentiable neural architecture search via reinforcement-learning-based meta-optimizer
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Biological underpinnings for lifelong learning machines
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Understanding and preventing capacity loss in reinforcement learning
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The primacy bias in deep reinforcement learning
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Loss of plasticity in continual deep reinforcement learning
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Archgym: An open-source gymnasium for machine learning assisted architecture design
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Learning activation functions for sparse neural networks
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Autorl hyperparameter landscapes
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Deep reinforcement learning with plasticity injection
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The dormant neuron phenomenon in deep reinforcement learning
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Interpretable concept bottlenecks to align reinforcement learning agents
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