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Found 12 papers, 1 papers with code
Sample Path Regularity of Gaussian Processes from the Covariance Kernel
While applications of GPs are myriad, a comprehensive understanding of GP sample paths, i. e. the function spaces over which they define a probability measure, is lacking.
Supervised structure learning
This paper concerns structure learning or discovery of discrete generative models.
On efficient computation in active inference
Despite being recognized as neurobiologically plausible, active inference faces difficulties when employed to simulate intelligent behaviour in complex environments due to its computational cost and the difficulty of specifying an appropriate target distribution for the agent.
Modelling non-reinforced preferences using selective attention
These memories are selectively attended to, using attention and gating blocks, to update agent's preferences.
Geometric Methods for Sampling, Optimisation, Inference and Adaptive Agents
In this chapter, we identify fundamental geometric structures that underlie the problems of sampling, optimisation, inference and adaptive decision-making.
Branching Time Active Inference: the theory and its generality
In this paper, we present an alternative framework that aims to unify tree search and active inference by casting planning as a structure learning problem.
Active inference, Bayesian optimal design, and expected utility
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
Under the Bayesian brain hypothesis, behavioural variations can be attributed to different priors over generative model parameters.
Reward Maximisation through Discrete Active Inference
Precisely, we show the conditions under which active inference produces the optimal solution to the Bellman equation--a formulation that underlies several approaches to model-based reinforcement learning and control.
Sophisticated Inference
In this paper, we consider a sophisticated kind of active inference, using a recursive form of expected free energy.
Neural dynamics under active inference: plausibility and efficiency of information processing
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.
