Search Results for author: Karl Friston

Found 14 papers, 3 papers with code

Geometric Methods for Sampling, Optimisation, Inference and Adaptive Agents

no code implementations20 Mar 2022 Alessandro Barp, Lancelot Da Costa, Guilherme França, Karl Friston, Mark Girolami, Michael I. Jordan, Grigorios A. Pavliotis

In this chapter, we identify fundamental geometric structures that underlie the problems of sampling, optimisation, inference and adaptive decision-making.

Decision Making

pymdp: A Python library for active inference in discrete state spaces

1 code implementation11 Jan 2022 Conor Heins, Beren Millidge, Daphne Demekas, Brennan Klein, Karl Friston, Iain Couzin, Alexander Tschantz

Active inference is an account of cognition and behavior in complex systems which brings together action, perception, and learning under the theoretical mantle of Bayesian inference.

Bayesian Inference

Insula Interoception, Active Inference and Feeling Representation

no code implementations23 Dec 2021 Alan S. R. Fermin, Karl Friston, Shigeto Yamawaki

The body sends interoceptive visceral information through deep brain structures to the cerebral cortex.

Active inference, Bayesian optimal design, and expected utility

no code implementations21 Sep 2021 Noor Sajid, Lancelot Da Costa, Thomas Parr, Karl Friston

Conversely, active inference reduces to Bayesian decision theory in the absence of ambiguity and relative risk, i. e., expected utility maximization.

Bayesian brains and the Rényi divergence

no code implementations12 Jul 2021 Noor Sajid, Francesco Faccio, Lancelot Da Costa, Thomas Parr, Jürgen Schmidhuber, Karl Friston

Under the Bayesian brain hypothesis, behavioural variations can be attributed to different priors over generative model parameters.

Bayesian Inference Variational Inference

Exploration and preference satisfaction trade-off in reward-free learning

no code implementations ICML Workshop URL 2021 Noor Sajid, Panagiotis Tigas, Alexey Zakharov, Zafeirios Fountas, Karl Friston

In this paper, we pursue the notion that this learnt behaviour can be a consequence of reward-free preference learning that ensures an appropriate trade-off between exploration and preference satisfaction.

OpenAI Gym

Active Tree Search in Large POMDPs

no code implementations25 Mar 2021 Domenico Maisto, Francesco Gregoretti, Karl Friston, Giovanni Pezzulo

Here, we introduce a novel method to plan in large POMDPs - Active Tree Search - that combines the normative character and biological realism of a leading planning theory in neuroscience (Active Inference) and the scalability of Monte-Carlo methods in AI.

Hippocampus

The relationship between dynamic programming and active inference: the discrete, finite-horizon case

no code implementations17 Sep 2020 Lancelot Da Costa, Noor Sajid, Thomas Parr, Karl Friston, Ryan Smith

In this paper, we consider the relation between active inference and dynamic programming under the Bellman equation, which underlies many approaches to reinforcement learning and control.

Decision Making reinforcement-learning

Action and Perception as Divergence Minimization

1 code implementation3 Sep 2020 Danijar Hafner, Pedro A. Ortega, Jimmy Ba, Thomas Parr, Karl Friston, Nicolas Heess

While the narrow objectives correspond to domain-specific rewards as typical in reinforcement learning, the general objectives maximize information with the environment through latent variable models of input sequences.

Decision Making Representation Learning

Sophisticated Inference

no code implementations7 Jun 2020 Karl Friston, Lancelot Da Costa, Danijar Hafner, Casper Hesp, Thomas Parr

In this paper, we consider a sophisticated kind of active inference, using a recursive form of expected free energy.

Active Learning

Deep active inference agents using Monte-Carlo methods

1 code implementation NeurIPS 2020 Zafeirios Fountas, Noor Sajid, Pedro A. M. Mediano, Karl Friston

In a more complex Animal-AI environment, our agents (using the same neural architecture) are able to simulate future state transitions and actions (i. e., plan), to evince reward-directed navigation - despite temporary suspension of visual input.

Neural dynamics under active inference: plausibility and efficiency of information processing

no code implementations22 Jan 2020 Lancelot Da Costa, Thomas Parr, Biswa Sengupta, Karl Friston

We then show that these neuronal dynamics approximate natural gradient descent, a well-known optimisation algorithm from information geometry that follows the steepest descent of the objective in information space.

Approximate Bayesian inference as a gauge theory

no code implementations17 May 2017 Biswa Sengupta, Karl Friston

In a published paper [Sengupta, 2016], we have proposed that the brain (and other self-organized biological and artificial systems) can be characterized via the mathematical apparatus of a gauge theory.

Bayesian Inference Variational Inference

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