Zohar Ringel

According to our database, Zohar Ringel authored at least 16 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Spectral-bias and kernel-task alignment in physically informed neural networks.

Inbar Seroussi, Asaf Miron, Zohar Ringel

Mach. Learn. Sci. Technol., 2024

Symmetric Kernels with Non-Symmetric Data: A Data-Agnostic Learnability Bound.

Itay Lavie, Zohar Ringel

CoRR, 2024

Wilsonian Renormalization of Neural Network Gaussian Processes.

Jessica N. Howard, Ro Jefferson, Anindita Maiti, Zohar Ringel

CoRR, 2024

Towards Understanding Inductive Bias in Transformers: A View From Infinity.

Itay Lavie, Guy Gur-Ari, Zohar Ringel

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Critical feature learning in deep neural networks.

Kirsten Fischer, Javed Lindner, David Dahmen, Zohar Ringel, Michael Krämer, Moritz Helias

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Grokking as a First Order Phase Transition in Two Layer Networks.

Noa Rubin, Inbar Seroussi, Zohar Ringel

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Droplets of Good Representations: Grokking as a First Order Phase Transition in Two Layer Networks.

Noa Rubin, Inbar Seroussi, Zohar Ringel

CoRR, 2023

Speed Limits for Deep Learning.

Inbar Seroussi, Alexander A. Alemi, Moritz Helias, Zohar Ringel

CoRR, 2023

2021

Separation of scales and a thermodynamic description of feature learning in some CNNs.

Inbar Seroussi, Zohar Ringel

CoRR, 2021

A self consistent theory of Gaussian Processes captures feature learning effects in finite CNNs.

Gadi Naveh, Zohar Ringel

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Relevance in the Renormalization Group and in Information Theory.

Amit Gordon, Aditya Banerjee, Maciej Koch-Janusz, Zohar Ringel

CoRR, 2020

Predicting the outputs of finite networks trained with noisy gradients.

Gadi Naveh, Oded Ben-David, Haim Sompolinsky, Zohar Ringel

CoRR, 2020

2019

Learning Curves for Deep Neural Networks: A Gaussian Field Theory Perspective.

Omry Cohen, Or Malka, Zohar Ringel

CoRR, 2019

The role of a layer in deep neural networks: a Gaussian Process perspective.

Oded Ben-David, Zohar Ringel

CoRR, 2019

2018

Critical Percolation as a Framework to Analyze the Training of Deep Networks.

Zohar Ringel, Rodrigo Andrade de Bem

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Mutual Information, Neural Networks and the Renormalization Group.

Maciej Koch-Janusz, Zohar Ringel

CoRR, 2017