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Category Archives: Minds & Machines
Criticality in deep neural nets
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
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Free energy, variational inference, and the brain
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
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Deep learning and the renormalization group
In recent years, a number of works have pointed to similarities between deep learning (DL) and the renormalization group (RG) [17]. This connection was originally made in the context of certain lattice models, where decimation RG bears a superficial resemblance … Continue reading
Posted in Minds & Machines, Physics
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Variational autoencoders
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 higherdimensional phase spaces. Of course, I couldn’t resist diving into the deeper mathematical theory … Continue reading
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Restricted Boltzmann machines
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
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Boltzmann machines
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
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Information geometry (part 3/3)
Insofar as quantum mechanics can be regarded as an extension of (classical) probability theory, most of the concepts developed in the previous two parts of this sequence can be extended to quantum information theory as well, thus giving rise to … Continue reading
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Information geometry (part 2/3)
In the previous post, we introduced the connection, and alluded to a dualistic structure between and . In particular, the cases are intimately related to two important families of statistical models, the exponential or efamily with affine connection , and … Continue reading
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Information geometry (part 1/3)
Information geometry is a rather interesting fusion of statistics and differential geometry, in which a statistical model is endowed with the structure of a Riemannian manifold. Each point on the manifold corresponds to a probability distribution function, and the metric … Continue reading
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