11 citations · 13 across the 3 of their papers we have counts for
8 papers
Reclaiming saliency: rhythmic precision-modulated action and perception
Ajith Anil Meera, Filip Novicky, Thomas Parr +3
Computational models of visual attention in artificial intelligence and robotics have been inspired by the concept of a saliency map. These models account for the mutual informatio…
Active inference, Bayesian optimal design, and expected utility
Noor Sajid, Lancelot Da Costa, Thomas Parr +1
Active inference, a corollary of the free energy principle, is a formal way of describing the behavior of certain kinds of random dynamical systems that have the appearance of sent…
Exploration and preference satisfaction trade-off in reward-free learning
Noor Sajid, Panagiotis Tigas, Alexey Zakharov +2
Biological agents have meaningful interactions with their environment despite the absence of immediate reward signals. In such instances, the agent can learn preferred modes of beh…
Bayesian brains and the Rényi divergence
Noor Sajid, Francesco Faccio, Lancelot Da Costa +3
Under the Bayesian brain hypothesis, behavioural variations can be attributed to different priors over generative model parameters. This provides a formal explanation for why indiv…
Deep active inference agents using Monte-Carlo methods
Zafeirios Fountas, Noor Sajid, Pedro A. M. Mediano +1
Active inference is a Bayesian framework for understanding biological intelligence. The underlying theory brings together perception and action under one single imperative: minimiz…
Active inference on discrete state-spaces: a synthesis
Lancelot Da Costa, Thomas Parr, Noor Sajid +3
Active inference is a normative principle underwriting perception, action, planning, decision-making and learning in biological or artificial agents. From its inception, its associ…