Whence the Expected Free Energy?
暂无分享,去创建一个
Beren Millidge | Alexander Tschantz | Christopher L Buckley | Beren Millidge | Alexander Tschantz | C. Buckley
[1] Taweh Beysolow II. Mathematical Review , 2020, Microwave and Wireless Synthesizers.
[2] Beren Millidge,et al. On the Relationship Between Active Inference and Control as Inference , 2020, IWAI.
[3] Anil K. Seth,et al. Reinforcement Learning through Active Inference , 2020, ArXiv.
[4] Tim Verbelen,et al. Learning Perception and Planning With Deep Active Inference , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[5] Karl J. Friston,et al. Active inference on discrete state-spaces: A synthesis , 2020, Journal of mathematical psychology.
[6] Alexander Tschantz,et al. Scaling Active Inference , 2019, 2020 International Joint Conference on Neural Networks (IJCNN).
[7] Beren Millidge,et al. Deep Active Inference as Variational Policy Gradients , 2019, Journal of Mathematical Psychology.
[8] Karl J. Friston,et al. Markov blankets, information geometry and stochastic thermodynamics , 2019, Philosophical Transactions of the Royal Society A.
[9] Karl J. Friston,et al. Generalised free energy and active inference , 2018, Biological Cybernetics.
[10] Anil K. Seth,et al. Learning action-oriented models through active inference , 2019, bioRxiv.
[11] Karl J. Friston. A free energy principle for a particular physics , 2019, 1906.10184.
[12] Bert de Vries,et al. Simulating Active Inference Processes by Message Passing , 2019, Frontiers Robotics AI.
[13] Thijs van de Laar,et al. Simulating Active Inference Processes by Message Passing , 2019, Front. Robot. AI.
[14] Beren Millidge. Implementing Predictive Processing and Active Inference: Preliminary Steps and Results , 2019 .
[15] Beren Millidge. Combining Active Inference and Hierarchical Predictive Coding: A Tutorial Introduction and Case Study , 2019 .
[16] Karl J. Friston,et al. Neuronal message passing using Mean-field, Bethe, and Marginal approximations , 2019, Scientific Reports.
[17] Karl J. Friston,et al. Impulsivity and Active Inference , 2019, Journal of Cognitive Neuroscience.
[18] Alexei A. Efros,et al. Large-Scale Study of Curiosity-Driven Learning , 2018, ICLR.
[19] Thomas Parr,et al. The computational neurology of active vision , 2019 .
[20] Kai Ueltzhöffer,et al. Deep active inference , 2017, Biological Cybernetics.
[21] Karl J. Friston,et al. Computational mechanisms of curiosity and goal-directed exploration , 2018, bioRxiv.
[22] Karl J. Friston,et al. Active Inference in OpenAI Gym: A Paradigm for Computational Investigations Into Psychiatric Illness. , 2018, Biological psychiatry. Cognitive neuroscience and neuroimaging.
[23] Stefan J. Kiebel,et al. Active Inference, Belief Propagation, and the Bethe Approximation , 2018, Neural Computation.
[24] Christoph Salge,et al. Expanding the Active Inference Landscape: More Intrinsic Motivations in the Perception-Action Loop , 2018, Front. Neurorobot..
[25] Sergey Levine,et al. Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review , 2018, ArXiv.
[26] Christopher L. Buckley,et al. A probabilistic interpretation of PID controllers using active inference , 2018, bioRxiv.
[27] Karl J. Friston,et al. Active inference and the anatomy of oculomotion , 2018, Neuropsychologia.
[28] Karl J. Friston,et al. The Computational Anatomy of Visual Neglect , 2017, Cerebral cortex.
[29] Karl J. Friston,et al. The graphical brain: Belief propagation and active inference , 2017, Network Neuroscience.
[30] Karl J. Friston,et al. Uncertainty, epistemics and active inference , 2017, Journal of The Royal Society Interface.
[31] Karl J. Friston,et al. Active Inference, Curiosity and Insight , 2017, Neural Computation.
[32] Karl J. Friston,et al. The active construction of the visual world , 2017, Neuropsychologia.
[33] Christopher L. Buckley,et al. An active inference implementation of phototaxis , 2017, ECAL.
[34] Karl J. Friston,et al. Deep temporal models and active inference , 2017, Neuroscience and biobehavioral reviews.
[35] Karl J. Friston,et al. Predicting green: really radical (plant) predictive processing , 2017, Journal of The Royal Society Interface.
[36] Simon McGregor,et al. The free energy principle for action and perception: A mathematical review , 2017, 1705.09156.
[37] Alexei A. Efros,et al. Curiosity-Driven Exploration by Self-Supervised Prediction , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[38] Evangelos A. Theodorou,et al. Model Predictive Path Integral Control: From Theory to Parallel Computation , 2017 .
[39] Karl J. Friston,et al. Active Inference: A Process Theory , 2017, Neural Computation.
[40] Stewart Shipp,et al. Neural Elements for Predictive Coding , 2016, Front. Psychol..
[41] Karl J. Friston,et al. Neuroscience and Biobehavioral Reviews , 2022 .
[42] Karl J. Friston,et al. Active Inference, epistemic value, and vicarious trial and error , 2016, Learning & memory.
[43] Karl J. Friston,et al. Scene Construction, Visual Foraging, and Active Inference , 2016, Front. Comput. Neurosci..
[44] David M. Blei,et al. Variational Inference: A Review for Statisticians , 2016, ArXiv.
[45] J. Schulman,et al. Variational Information Maximizing Exploration , 2016 .
[46] Shakir Mohamed,et al. Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning , 2015, NIPS.
[47] Karl J. Friston,et al. Cerebral hierarchies: predictive processing, precision and the pulvinar , 2015, Philosophical Transactions of the Royal Society B: Biological Sciences.
[48] Karl J. Friston,et al. Active inference and epistemic value , 2015, Cognitive neuroscience.
[49] Raymond J. Dolan,et al. Active Inference, Evidence Accumulation, and the Urn Task , 2015, Neural Computation.
[50] Sophie Denève,et al. Bayesian Inference with Spiking Neurons , 2004, Encyclopedia of Computational Neuroscience.
[51] K. Rawlik. On probabilistic inference approaches to stochastic optimal control , 2013 .
[52] Raymond J. Dolan,et al. Exploration, novelty, surprise, and free energy minimization , 2013, Front. Psychol..
[53] Evangelos Theodorou,et al. Relative entropy and free energy dualities: Connections to Path Integral and KL control , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).
[54] Karl J. Friston,et al. Canonical Microcircuits for Predictive Coding , 2012, Neuron.
[55] Stefan J. Kiebel,et al. Evidence for neural encoding of Bayesian surprise in human somatosensation , 2012, NeuroImage.
[56] Marc Toussaint,et al. On Stochastic Optimal Control and Reinforcement Learning by Approximate Inference , 2012, Robotics: Science and Systems.
[57] Karl J. Friston,et al. Free Energy, Value, and Attractors , 2011, Comput. Math. Methods Medicine.
[58] Stephen J. Roberts,et al. A tutorial on variational Bayesian inference , 2012, Artificial Intelligence Review.
[59] Doina Precup,et al. An information-theoretic approach to curiosity-driven reinforcement learning , 2012, Theory in Biosciences.
[60] Karl J. Friston. What Is Optimal about Motor Control? , 2011, Neuron.
[61] Yi Sun,et al. Planning to Be Surprised: Optimal Bayesian Exploration in Dynamic Environments , 2011, AGI.
[62] Karl J. Friston,et al. Action understanding and active inference , 2011, Biological Cybernetics.
[63] Pierre Baldi,et al. Of bits and wows: A Bayesian theory of surprise with applications to attention , 2010, Neural Networks.
[64] Stefan Schaal,et al. A Generalized Path Integral Control Approach to Reinforcement Learning , 2010, J. Mach. Learn. Res..
[65] Karl J. Friston. The free-energy principle: a unified brain theory? , 2010, Nature Reviews Neuroscience.
[66] Karl J. Friston,et al. Reinforcement Learning or Active Inference? , 2009, PloS one.
[67] Pierre Baldi,et al. Bayesian surprise attracts human attention , 2005, Vision Research.
[68] Marc Toussaint,et al. Probabilistic inference as a model of planned behavior , 2009, Künstliche Intell..
[69] Michael I. Jordan,et al. Graphical Models, Exponential Families, and Variational Inference , 2008, Found. Trends Mach. Learn..
[70] Karl J. Friston. Hierarchical Models in the Brain , 2008, PLoS Comput. Biol..
[71] Michael W. Spratling. Reconciling Predictive Coding and Biased Competition Models of Cortical Function , 2008, Frontiers Comput. Neurosci..
[72] Karl J. Friston. Variational filtering , 2008, NeuroImage.
[73] Karl J. Friston,et al. DEM: A variational treatment of dynamic systems , 2008, NeuroImage.
[74] Pierre-Yves Oudeyer,et al. What is Intrinsic Motivation? A Typology of Computational Approaches , 2007, Frontiers Neurorobotics.
[75] H. Kappen. An introduction to stochastic control theory, path integrals and reinforcement learning , 2007 .
[76] Rajesh P. N. Rao,et al. Bayesian brain : probabilistic approaches to neural coding , 2006 .
[77] Karl J. Friston,et al. A free energy principle for the brain , 2006, Journal of Physiology-Paris.
[78] Tutut Herawan,et al. Computational and mathematical methods in medicine. , 2006, Computational and mathematical methods in medicine.
[79] William T. Freeman,et al. Constructing free-energy approximations and generalized belief propagation algorithms , 2005, IEEE Transactions on Information Theory.
[80] H. Kappen. Path integrals and symmetry breaking for optimal control theory , 2005, physics/0505066.
[81] Karl J. Friston,et al. A theory of cortical responses , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.
[82] D. Knill,et al. The Bayesian brain: the role of uncertainty in neural coding and computation , 2004, Trends in Neurosciences.
[83] Amos Storkey,et al. Advances in Neural Information Processing Systems 20 , 2007 .
[84] Karl J. Friston. Learning and inference in the brain , 2003, Neural Networks.
[85] Hagai Attias,et al. Planning by Probabilistic Inference , 2003, AISTATS.
[86] Matthew J. Beal. Variational algorithms for approximate Bayesian inference , 2003 .
[87] W. Freeman,et al. Generalized Belief Propagation , 2000, NIPS.