This review considers computational psychiatry from a particular viewpoint: namely, a
commitment to explaining psychopathology in terms of pathophysiology. It rests on the …

Variational autoencoders (VAEs) are powerful deep generative models widely used to
represent high-dimensional complex data through a low-dimensional latent space learned …

The first comprehensive treatment of active inference, an integrative perspective on brain,
cognition, and behavior used across multiple disciplines. Active inference is a way of …

INTERNATIONAL BESTSELLER A Best Book of 2021—Bloomberg Businessweek; A Best
Science Book of 2021—The Guardian; A Best Science Book of 2021—Financial Times; A …

This paper is a broad and accessible survey of the methods we have at our disposal for
Monte Carlo gradient estimation in machine learning and across the statistical sciences: the …

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 …

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. The …

Prominent accounts of sentient behavior depict brains as generative models of organismic
interaction with the world, evincing intriguing similarities with current advances in generative …

The active inference framework, and in particular its recent formulation as a partially
observable Markov decision process (POMDP), has gained increasing popularity in recent …

We propose a new regularization method based on virtual adversarial loss: a new measure
of local smoothness of the conditional label distribution given input. Virtual adversarial loss …