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“A theory of local learning, the learning channel, and the optimality of backpropagation”
Pierre Baldi and Peter Sadowski · 2016
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Alexandra Constantinescu, Jill O’Reilly and Timothy Behrens · 2016
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“Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation”
Tejas Kulkarni, Karthik Narasimhan, Ardavan Saeedi and Josh Tenenbaum · 2016
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“How important is weight symmetry in backpropagation?”
Qianli Liao, Joel Leibo and Tomaso Poggio · 2016
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“Toward an integration of deep learning and neuroscience”
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S Wolf · 2004
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“Intrinsically motivated reinforcement learning”
Nuttapong Chentanez, Andrew Barto and Satinder Singh · 2005
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Kent Berridge · 2007
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“Artificial general intelligence”
Ben Goertzel and Cassio Pennachin · 2007
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Jin-Hee Han et al · 2007
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Geoffrey Hinton · 2007
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H Markram and B Sakmann · 2007
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Adam Marblestone, Greg Wayne and Konrad Kording · 2016
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“Mastering the game of Go with deep neural networks and tree search”
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Marcin Andrychowicz et al · 2017
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“Neuroscience-inspired artificial intelligence”
Demis Hassabis, Dharshan Kumaran, Christopher Summerfield and Matthew Botvinick · 2017
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“Decoupled neural interfaces using synthetic gradients”
Max Jaderberg et al · 2017
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“Overcoming catastrophic forgetting in neural networks”
James Kirkpatrick et al · 2017
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“Building machines that learn and think like people”
Brenden Lake, Tomer Ullman, Joshua Tenenbaum and Samuel Gershman · 2017
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“Understanding principles of integration and segregation using whole-brain computational connectomics: implications for neuropsychiatric disorders”
Louis-David Lord, Angus Stevner, Gustavo Deco and Morten Kringelbach · 2017
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“Evolution strategies as a scalable alternative to reinforcement learning”
Tim Salimans et al · 2017
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“A simple neural network module for relational reasoning”
Adam Santoro et al · 2017
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“Architecture, function, and assembly of the mouse visual system”
Tania Seabrook, Timothy Burbridge, Michael Crair and Andrew Huberman · 2017
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“Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning”
Victor Zhong, Caiming Xiong and Richard Socher · 2017
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“Threat of adversarial attacks on deep learning in computer vision: A survey”
Naveed Akhtar and Ajmal Mian · 2018
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“Learning in the machine: Recirculation is random backpropagation”
Pierre Baldi and Peter Sadowski · 2018
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“Vector-based navigation using grid-like representations in artificial agents”
Andrea Banino et al · 2018
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“A framework for intelligence and cortical function based on grid cells in the neocortex”
Jeff Hawkins et al · 2018
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“Deep learning: A critical appraisal”
Gary Marcus · 2018
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“A deep learning approach to identifying source code in images and video”
Jordan Ott et al · 2018
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“Learning lexical features of programming languages from imagery using convolutional neural networks”
Jordan Ott et al · 2018
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“Relational recurrent neural networks”
Adam Santoro et al · 2018
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“Reinforcement learning: An introduction”
Richard Sutton and Andrew Barto · 2018
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“A spiking neural network framework for robust sound classification”
Jibin Wu et al · 2018
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Ilge Akkaya et al · 2019
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Spyridon Karadimas et al · 2019
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Arild Nøkland and Lars Eidnes · 2019
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Jordan Ott · 2019
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Jiawei Su, Danilo Vargas and Kouichi Sakurai · 2019
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Xiaoyong Yuan, Pan He, Qile Zhu and Xiaolin Li · 2019
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