2 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.AI2021
Interpreting Dynamical Systems as Bayesian Reasoners
Nathaniel Virgo, Martin Biehl, Simon McGregor
A central concept in active inference is that the internal states of a physical system parametrise probability measures over states of the external world. These can be seen as an a…
cs.AI2016★ 2 cited
Neural Coarse-Graining: Extracting slowly-varying latent degrees of freedom with neural networks
Nicholas Guttenberg, Martin Biehl, Ryota Kanai
We present a loss function for neural networks that encompasses an idea of trivial versus non-trivial predictions, such that the network jointly determines its own prediction goals…