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Found 9 papers, 3 papers with code
Navigation under uncertainty: Trajectory prediction and occlusion reasoning with switching dynamical systems
Predicting future trajectories of nearby objects, especially under occlusion, is a crucial task in autonomous driving and safe robot navigation.
Gradient-free variational learning with conditional mixture networks
Previous work shows that fast variational methods can reduce the compute requirements of Bayesian methods by eliminating the need for gradient computation or sampling, but are often limited to simple models.
From pixels to planning: scale-free active inference
This paper describes a discrete state-space model -- and accompanying methods -- for generative modelling.
Supervised structure learning
This paper concerns structure learning or discovery of discrete generative models.
Designing Ecosystems of Intelligence from First Principles
In this context, we understand intelligence as the capacity to accumulate evidence for a generative model of one's sensed world -- also known as self-evidencing.
Knitting a Markov blanket is hard when you are out-of-equilibrium: two examples in canonical nonequilibrium models
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 facilitating inference-like behaviour in living systems.
Particular flows and attracting sets: A comment on "How particular is the physics of the Free Energy Principle?" by Aguilera, Millidge, Tschantz and Buckley
However, in this commentary I focus on the flow of particular states (internal and blanket states) and their variational free energy gradients, and show that for a wide but restricted class of solenoidal couplings, the average flow of these systems point along variational free energy gradients.
pymdp: A Python library for active inference in discrete state spaces
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.
Sparse convolutional coding for neuronal assembly detection
Cell assemblies, originally proposed by Donald Hebb (1949), are subsets of neurons firing in a temporally coordinated way that gives rise to repeated motifs supposed to underly neural representations and information processing.
