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2020 – today
- 2024
[j1]Inbar Seroussi
, Asaf Miron, Zohar Ringel:
Spectral-bias and kernel-task alignment in physically informed neural networks. Mach. Learn. Sci. Technol. 5(3): 35048 (2024)- [c5]Noa Rubin, Inbar Seroussi, Zohar Ringel:
Grokking as a First Order Phase Transition in Two Layer Networks. ICLR 2024 - [c4]Kirsten Fischer, Javed Lindner, David Dahmen, Zohar Ringel, Michael Krämer, Moritz Helias:
Critical feature learning in deep neural networks. ICML 2024 - [c3]Itay Lavie, Guy Gur-Ari, Zohar Ringel:
Towards Understanding Inductive Bias in Transformers: A View From Infinity. ICML 2024 - [i13]Itay Lavie, Guy Gur-Ari, Zohar Ringel:
Towards Understanding Inductive Bias in Transformers: A View From Infinity. CoRR abs/2402.05173 (2024) - [i12]Jessica N. Howard, Ro Jefferson, Anindita Maiti, Zohar Ringel:
Wilsonian Renormalization of Neural Network Gaussian Processes. CoRR abs/2405.06008 (2024) - [i11]Itay Lavie, Zohar Ringel:
Symmetric Kernels with Non-Symmetric Data: A Data-Agnostic Learnability Bound. CoRR abs/2406.02663 (2024) - 2023
- [i10]Inbar Seroussi, Asaf Miron, Zohar Ringel:
Spectral-Bias and Kernel-Task Alignment in Physically Informed Neural Networks. CoRR abs/2307.06362 (2023) - [i9]Inbar Seroussi, Alexander A. Alemi, Moritz Helias, Zohar Ringel:
Speed Limits for Deep Learning. CoRR abs/2307.14653 (2023) - [i8]Noa Rubin, Inbar Seroussi, Zohar Ringel:
Droplets of Good Representations: Grokking as a First Order Phase Transition in Two Layer Networks. CoRR abs/2310.03789 (2023) - 2021
- [c2]Gadi Naveh, Zohar Ringel:
A self consistent theory of Gaussian Processes captures feature learning effects in finite CNNs. NeurIPS 2021: 21352-21364 - [i7]Gadi Naveh, Zohar Ringel:
A self consistent theory of Gaussian Processes captures feature learning effects in finite CNNs. CoRR abs/2106.04110 (2021) - [i6]Inbar Seroussi, Zohar Ringel:
Separation of scales and a thermodynamic description of feature learning in some CNNs. CoRR abs/2112.15383 (2021) - 2020
- [i5]Gadi Naveh, Oded Ben-David, Haim Sompolinsky, Zohar Ringel:
Predicting the outputs of finite networks trained with noisy gradients. CoRR abs/2004.01190 (2020) - [i4]Amit Gordon, Aditya Banerjee, Maciej Koch-Janusz, Zohar Ringel:
Relevance in the Renormalization Group and in Information Theory. CoRR abs/2012.01447 (2020)
2010 – 2019
- 2019
- [i3]Oded Ben-David, Zohar Ringel:
The role of a layer in deep neural networks: a Gaussian Process perspective. CoRR abs/1902.02354 (2019) - [i2]Omry Cohen, Or Malka, Zohar Ringel:
Learning Curves for Deep Neural Networks: A Gaussian Field Theory Perspective. CoRR abs/1906.05301 (2019) - 2018
- [c1]Zohar Ringel, Rodrigo Andrade de Bem:
Critical Percolation as a Framework to Analyze the Training of Deep Networks. ICLR (Poster) 2018 - 2017
- [i1]Maciej Koch-Janusz, Zohar Ringel:
Mutual Information, Neural Networks and the Renormalization Group. CoRR abs/1704.06279 (2017)
Coauthor Index
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