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Active inference is a Bayesian framework for understanding biological intelligence.
Counting backward during chess move choice
Dennis H Holding · 1989
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The attention system of the human brain
Michael I Posner and Steven E Petersen · 1990
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Learning and selective attention
Peter Dayan, Sham Kakade, and Read P Montague · 2000
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ACh, uncertainty, and cortical inference
Peter Dayan and Angela J Yu · 2002
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Neuromodulatory transmitter systems in the cortex and their role in cortical plasticity
Q Gu · 2002
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The effects of speed on skilled chess performance
Bruce D Burns · 2004
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Uncertainty, neuromodulation, and attention
Angela J Yu and Peter Dayan · 2005
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Efficient selectivity and backup operators in monte-carlo tree search
Rémi Coulom · 2006
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Bandit based Monte-Carlo planning
Levente Kocsis and Csaba Szepesvári · 2006
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The effects of time pressure on chess skill: An investigation into fast and slow processes underlying expert performance
Frenk Van Harreveld, Eric-Jan Wagenmakers, and Han LJ Van Der Maas · 2007
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Biologically inspired framework for learning and abstract representation of attention control
Hadi Fatemi Shariatpanahi and Majid Nili Ahmadabadi · 2007
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Reinforcement learning or active inference?
Karl J Friston, Jean Daunizeau, and Stefan J Kiebel · 2009
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The free-energy principle: A unified brain theory?
Karl J Friston · 2010
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Advances in visual perceptual learning and plasticity
Yuka Sasaki, Jose E Nanez, and Takeo Watanabe · 2010
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Mechanisms of top-down attention
Farhan Baluch and Laurent Itti · 2011
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A survey of Monte Carlo tree search methods
Cameron B Browne, Edward Powley, Daniel Whitehouse, Simon M Lucas, Peter I Cowling, Philipp Rohlfshagen, Stephen Tavener, Diego Perez, Spyridon Samothrakis, and Simon Colton · 2012
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Information processing in decision-making systems
Matthijs Van Der Meer, Zeb Kurth-Nelson, and A David Redish · 2012
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Exploring the relationship between perceptual learning and top-down attentional control
Anna Byers and John T. Serences · 2012
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Bonsai trees in your head: How the Pavlovian system sculpts goal-directed choices by pruning decision trees
Quentin JM Huys, Neir Eshel, Elizabeth O’Nions, Luke Sheridan, Peter Dayan, and Jonathan P Roiser · 2012
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Predictions not commands: Active inference in the motor system
Rick A Adams, Stewart Shipp, and Karl J Friston · 2013
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Free energy, precision and learning: The role of cholinergic neuromodulation
Rosalyn J Moran, Pablo Campo, Mkael Symmonds, Klaas E Stephan, Raymond J Dolan, and Karl J Friston · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Deep learning for real-time Atari game play using offline Monte-Carlo tree search planning
Xiaoxiao Guo, Satinder Singh, Honglak Lee, Richard L Lewis, and Xiaoshi Wang · 2014
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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 Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
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Prospective optimization with limited resources
Joseph Snider, Dongpyo Lee, Howard Poizner, and Sergei Gepshtein · 2015
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Evidence integration in model-based tree search
Alec Solway and Matthew M Botvinick · 2015
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Active inference and learning
Karl J Friston, Thomas. FitzGerald, Francesco Rigoli, Philipp Schwartenbeck, John O’Doherty, and Giovanni Pezzulo · 2016
Cited alongside, same era.
Dropout as a Bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
Cited alongside, same era.
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
In vitro neural networks minimise variational free energy
Takuya Isomura and Karl J Friston · 2018
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Disentangling disentanglement in variational autoencoders
Emile Mathieu, Tom Rainforth, N Siddharth, and Yee Whye Teh · 2018
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Precision and false perceptual inference
Thomas Parr, David A. Benrimoh, Peter Vincent, and Karl J Friston · 2018
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Reinforcement learning and control as probabilistic inference: Tutorial and review
Sergey Levine · 2018
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The computational anatomy of visual neglect
Thomas Parr and Karl J Friston · 2018
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Cited alongside, same era.
Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
Cited alongside, same era.
Uncertainty, epistemics and active inference
Thomas Parr and Karl J Friston · 2017
Cited alongside, same era.
Variational inference: A review for statisticians
David M Blei, Alp Kucukelbir, and Jon D McAuliffe · 2017
Cited alongside, same era.
Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
Cited alongside, same era.
beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
Cited alongside, same era.
dsprites: Disentanglement testing sprites dataset
Loic Matthey, Irina Higgins, Demis Hassabis, and Alexander Lerchner · 2017
Cited alongside, same era.
Karl J Friston · 2019
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Active inference: Demystified and compared
Noor Sajid, Philip J Ball, and Karl J Friston · 2019
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Deeply felt affect: The emergence of valence in deep active inference
Casper Hesp, Ryan Smith, Micah Allen, Karl J Friston, and Maxwell Ramstead · 2019
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Bayesian policy selection using active inference
Ozan Çatal, Johannes Nauta, Tim Verbelen, Pieter Simoens, and Bart Dhoedt · 2019
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Alexander Tschantz, Manuel Baltieri, Anil Seth, Christopher L Buckley, et al · 2019
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Generalised free energy and active inference
Thomas Parr and Karl J Friston · 2019
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Computational mechanisms of curiosity and goal-directed exploration
Philipp Schwartenbeck, Johannes Passecker, Tobias U Hauser, Thomas HB FitzGerald, Martin Kronbichler, and Karl J Friston · 2019
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An inference perspective on model-based reinforcement learning
Joseph Marino and Yisong Yue · 2019
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The Animal-AI olympics
Matthew Crosby, Benjamin Beyret, and Marta Halina · 2019
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Bayes-Factor-VAE: Hierarchical Bayesian deep auto-encoder models for factor disentanglement
Minyoung Kim, Yuting Wang, Pritish Sahu, and Vladimir Pavlovic · 2019
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Towards interpretable reinforcement learning using attention augmented agents
Alexander Mott, Daniel Zoran, Mike Chrzanowski, Daan Wierstra, and Danilo J Rezende · 2019
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The Computational Neurology of Active Vision
Thomas Parr · 2019
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Virel: A variational inference framework for reinforcement learning
Matthew Fellows, Anuj Mahajan, Tim GJ Rudner, and Shimon Whiteson · 2019
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Active inference on discrete state-spaces: A synthesis
Lancelot Da Costa, Thomas Parr, Noor Sajid, Sebastijan Veselic, Victorita Neacsu, and Karl Friston · 2020
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Deep active inference as variational policy gradients
Beren Millidge · 2020
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Alexander Tschantz, Beren Millidge, Anil K Seth, and Christopher L Buckley · 2020
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Learning perception and planning with deep active inference
Ozan Çatal, Tim Verbelen, Johannes Nauta, Cedric De Boom, and Bart Dhoedt · 2020
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