Mutual Information, Neural Networks and the Renormalization Group. - Maciej Koch-Janusz; Zohar Ringel (CoRR, 2017)

Critical Percolation as a Framework to Analyze the Training of Deep Networks. - Zohar Ringel; Rodrigo Andrade de Bem (ICLR (Poster), 2018)

The role of a layer in deep neural networks: a Gaussian Process perspective. - Oded Ben-David; Zohar Ringel (CoRR, 2019)

Learning Curves for Deep Neural Networks: A Gaussian Field Theory Perspective. - Omry Cohen; Or Malka; Zohar Ringel (CoRR, 2019)

Predicting the outputs of finite networks trained with noisy gradients. - Gadi Naveh; Oded Ben-David; Haim Sompolinsky; Zohar Ringel (CoRR, 2020)

Relevance in the Renormalization Group and in Information Theory. - Amit Gordon; Aditya Banerjee; Maciej Koch-Janusz; Zohar Ringel (CoRR, 2020)

A self consistent theory of Gaussian Processes captures feature learning effects in finite CNNs. - Gadi Naveh; Zohar Ringel (NeurIPS, 2021)

Separation of scales and a thermodynamic description of feature learning in some CNNs. - Inbar Seroussi; Zohar Ringel (CoRR, 2021)

Speed Limits for Deep Learning. - Inbar Seroussi; Alexander A. Alemi; Moritz Helias; Zohar Ringel (CoRR, 2023)

Droplets of Good Representations: Grokking as a First Order Phase Transition in Two Layer Networks. - Noa Rubin; Inbar Seroussi; Zohar Ringel (CoRR, 2023)

Grokking as a First Order Phase Transition in Two Layer Networks. - Noa Rubin; Inbar Seroussi; Zohar Ringel (ICLR, 2024)

Critical feature learning in deep neural networks. - Kirsten Fischer; Javed Lindner; David Dahmen; Zohar Ringel; Michael Krämer; Moritz Helias (ICML, 2024)

Towards Understanding Inductive Bias in Transformers: A View From Infinity. - Itay Lavie; Guy Gur-Ari; Zohar Ringel (ICML, 2024)

Wilsonian Renormalization of Neural Network Gaussian Processes. - Jessica N. Howard; Ro Jefferson; Anindita Maiti; Zohar Ringel (CoRR, 2024)

Symmetric Kernels with Non-Symmetric Data: A Data-Agnostic Learnability Bound. - Itay Lavie; Zohar Ringel (CoRR, 2024)

Spectral-bias and kernel-task alignment in physically informed neural networks. - Inbar Seroussi; Asaf Miron; Zohar Ringel (Mach. Learn. Sci. Technol., 2024)

From Kernels to Features: A Multi-Scale Adaptive Theory of Feature Learning. - Noa Rubin; Kirsten Fischer; Javed Lindner; David Dahmen; Inbar Seroussi; Zohar Ringel; Michael Krämer; Moritz Helias (CoRR, 2025)

Applications of Statistical Field Theory in Deep Learning. - Zohar Ringel; Noa Rubin; Edo Mor; Moritz Helias; Inbar Seroussi (CoRR, 2025)