161 citations · 528 across the 21 of their papers we have counts for
25 papers
Variational Filtering for Bayesian Last Layers with an Independent Binary Probit Surrogate
Dimitrije Markovic, Conor Heins, Alexander Tschantz +1
Modern deep classifiers trained by gradient descent need many passes over the data to become accurate, and even then are not always well calibrated; post-hoc temperature scaling co…
Collective Sensing as Emergent Bayesian Inference
Conor Heins
Collective behavior in animal groups is routinely described in cognitive language: fish schools sense gradients, swarms compute, colonies decide. These formulations often leave ope…
A Hardware-oriented Approach for Efficient Bayesian Inference Computation and Deployment
Nikola Pižurica, Matteo Risso, Nikola Milović +4
Bayesian inference provides a principled foundation for reasoning under uncertainty, but its computational cost hinders deployment on resource-constrained edge devices. In this pap…
Active inference and artificial reasoning
Karl Friston, Lancelot Da Costa, Alexander Tschantz +4
This technical note considers the sampling of outcomes that provide the greatest amount of information about the structure of underlying world models. This generalisation furnishes…
Soft Geometric Inductive Bias for Object Centric Dynamics
Hampus Linander, Conor Heins, Alexander Tschantz +2
Physical systems are naturally parameterized in terms of geometric entities and their transformations. While exact equivariance can be a powerful inductive bias, real-world applica…
A Hardware-oriented Approach for Efficient Active Inference Computation and Deployment
Nikola Pižurica, Nikola Milović, Igor Jovančević +2
Active Inference (AIF) offers a robust framework for decision-making, yet its computational and memory demands pose challenges for deployment, especially in resource-constrained en…
AXIOM: Learning to Play Games in Minutes with Expanding Object-Centric Models
Conor Heins, Toon Van de Maele, Alexander Tschantz +11
Current deep reinforcement learning (DRL) approaches achieve state-of-the-art performance in various domains, but struggle with data efficiency compared to human learning, which le…
Bayesian Predictive Coding
Alexander Tschantz, Magnus Koudahl, Hampus Linander +4
Predictive coding (PC) is an influential theory of information processing in the brain, providing a biologically plausible alternative to backpropagation. It is motivated in terms…
Navigation under uncertainty: Trajectory prediction and occlusion reasoning with switching dynamical systems
Ran Wei, Joseph Lee, Shohei Wakayama +10
Predicting future trajectories of nearby objects, especially under occlusion, is a crucial task in autonomous driving and safe robot navigation. Prior works typically neglect to ma…
A theory of generalised coordinates for stochastic differential equations
Lancelot Da Costa, Nathaël Da Costa, Conor Heins +5
Stochastic differential equations are ubiquitous modelling tools in physics and the sciences. In most modelling scenarios, random fluctuations driving dynamics or motion have some…
Gradient-free variational learning with conditional mixture networks
Conor Heins, Hao Wu, Dimitrije Markovic +3
Balancing computational efficiency with robust predictive performance is crucial in supervised learning, especially for critical applications. Standard deep learning models, while…
From pixels to planning: scale-free active inference
Karl Friston, Conor Heins, Tim Verbelen +7
This paper describes a discrete state-space model -- and accompanying methods -- for generative modelling. This model generalises partially observed Markov decision processes to in…
Supervised structure learning
Karl J. Friston, Lancelot Da Costa, Alexander Tschantz +10
This paper concerns structure learning or discovery of discrete generative models. It focuses on Bayesian model selection and the assimilation of training data or content, with a s…
Collective behavior from surprise minimization
Conor Heins, Beren Millidge, Lancelot da Costa +3
Collective motion is ubiquitous in nature; groups of animals, such as fish, birds, and ungulates appear to move as a whole, exhibiting a rich behavioral repertoire that ranges from…
An analytical model of active inference in the Iterated Prisoner's Dilemma
Daphne Demekas, Conor Heins, Brennan Klein
This paper addresses a mathematically tractable model of the Prisoner's Dilemma using the framework of active inference. In this work, we design pairs of Bayesian agents that are t…
Designing Ecosystems of Intelligence from First Principles
Karl J Friston, Maxwell J D Ramstead, Alex B Kiefer +17
This white paper lays out a vision of research and development in the field of artificial intelligence for the next decade (and beyond). Its denouement is a cyber-physical ecosyste…
Path integrals, particular kinds, and strange things
Karl Friston, Lancelot Da Costa, Dalton A. R. Sakthivadivel +4
This paper describes a path integral formulation of the free energy principle. The ensuing account expresses the paths or trajectories that a particle takes as it evolves over time…
Knitting a Markov blanket is hard when you are out-of-equilibrium: two examples in canonical nonequilibrium models
Miguel Aguilera, Ángel Poc-López, Conor Heins +1
Bayesian theories of biological and brain function speculate that Markov blankets (a conditional independence separating a system from external states) play a key role for facilita…
Spin glass systems as collective active inference
Conor Heins, Brennan Klein, Daphne Demekas +2
An open question in the study of emergent behaviour in multi-agent Bayesian systems is the relationship, if any, between individual and collective inference. In this paper we explo…
Sparse coupling and Markov blankets: A comment on "How particular is the physics of the Free Energy Principle?" by Aguilera, Millidge, Tschantz and Buckley
Conor Heins, Lancelot Da Costa
In this commentary, we respond to a technical analysis of the Free Energy Principle (hereafter: FEP) presented in "How particular is the physics of the Free Energy Principle" by Ag…
Particular flows and attracting sets: A comment on "How particular is the physics of the Free Energy Principle?" by Aguilera, Millidge, Tschantz and Buckley
Conor Heins
In this commentary, I expand on the analysis of the recent article "How particular is the physics of the Free Energy Principle?" by Aguilera et al. by studying the flow fields of l…
On Bayesian Mechanics: A Physics of and by Beliefs
Maxwell J. D. Ramstead, Dalton A. R. Sakthivadivel, Conor Heins +5
The aim of this paper is to introduce a field of study that has emerged over the last decade called Bayesian mechanics. Bayesian mechanics is a probabilistic mechanics, comprising…
The free energy principle made simpler but not too simple
Karl Friston, Lancelot Da Costa, Noor Sajid +4
This paper provides a concise description of the free energy principle, starting from a formulation of random dynamical systems in terms of a Langevin equation and ending with a Ba…
pymdp: A Python library for active inference in discrete state spaces
Conor Heins, Beren Millidge, Daphne Demekas +4
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 inferenc…