Category Archives: Minds & Machines

In the previous post, we introduced mean field theory (MFT) as a means of approximating the partition function for interacting systems. In particular, we used this to determine the critical point at which the system undergoes a phase transition, and … Continue reading →

In several recent posts, I explored various ideas that lie at the interface of physics, information theory, and machine learning: We’ve seen, à la Jaynes, how the concepts of entropy in statistical thermodynamics and information theory are unified, perhaps the … Continue reading →

As part of one of my current research projects, I’ve been looking into variational autoencoders (VAEs) for the purpose of identifying and analyzing attractor solutions within higher-dimensional phase spaces. Of course, I couldn’t resist diving into the deeper mathematical theory … Continue reading →

As a theoretical physicist making their first foray into machine learning, one is immediately captivated by the fascinating parallel between deep learning and the renormalization group. In essence, both are concerned with the extraction of relevant features via a process … Continue reading →

I alluded previously that information geometry had many interesting applications, among them machine learning and computational neuroscience more generally. A classic example is the original paper by Amari, Kurata, and Nagaoka, Information Geometry of Boltzmann Machines [1]. This paper has … Continue reading →