1.2k citations · 2.2k across the 46 of their papers we have counts for
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Separation of Scales and a Thermodynamic Description of Feature Learning in Some CNNs
Inbar Seroussi, Gadi Naveh, Zohar Ringel
Deep neural networks (DNNs) are powerful tools for compressing and distilling information. Their scale and complexity, often involving billions of inter-dependent parameters, rende…
A self consistent theory of Gaussian Processes captures feature learning effects in finite CNNs
Gadi Naveh, Zohar Ringel
Deep neural networks (DNNs) in the infinite width/channel limit have received much attention recently, as they provide a clear analytical window to deep learning via mappings to Ga…
Symmetries and phase diagrams with real-space mutual information neural estimation
Doruk Efe Gökmen, Zohar Ringel, Sebastian D. Huber +1
Real-space mutual information (RSMI) was shown to be an important quantity, formally and from a numerical standpoint, in finding coarse-grained descriptions of physical systems. It…
Statistical physics through the lens of real-space mutual information
Doruk Efe Gökmen, Zohar Ringel, Sebastian D. Huber +1
Identifying the relevant coarse-grained degrees of freedom in a complex physical system is a key stage in developing powerful effective theories in and out of equilibrium. The cele…