Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …

Inverse problems in statistical physics are motivated by the challenges of 'big data'in
different fields, in particular high-throughput experiments in biology. In inverse problems, the …

Diffusion models excel at capturing complex data distributions, such as those of natural
images and proteins. While diffusion models are trained to represent the distribution in the …

With the goal to generate more scalable algorithms with higher efficiency and fewer open
parameters, reinforcement learning (RL) has recently moved towards combining classical …

This paper describes a general model that subsumes many parametric models for
continuous data. The model comprises hidden layers of state-space or dynamic causal …

Optimal control of diffusion processes is intimately connected to the problem of solving
certain Hamilton–Jacobi–Bellman equations. Building on recent machine learning inspired …

We reformulate a class of non-linear stochastic optimal control problems introduced by
Todorov (in Advances in Neural Information Processing Systems, vol. 19, pp. 1369–1376 …

The expected free energy (EFE) is a central quantity in the theory of active inference. It is the
quantity that all active inference agents are mandated to minimize through action, and its …

This paper provides an overview of the reinforcement learning and optimal adaptive control
literature and its application to robotics. Reinforcement learning is bridging the gap between …

Reinforcement learning (RL) is one of the most general approaches to learning control. Its
applicability to complex motor systems, however, has been largely impossible so far due to …