Zohar Ringel - RankMe

25 papers A* 5Journal 19Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Algorithmic Capture, Computational Complexity, and Inductive Bias of Infinite Transformers. CoRR Orit Davidovich, Zohar Ringel
2025	J	`jnl`	Applications of Statistical Field Theory in Deep Learning. CoRR Zohar Ringel, Noa Rubin, Edo Mor, Moritz Helias, Inbar Seroussi
2025	A*	`conf`	From Kernels to Features: A Multi-Scale Adaptive Theory of Feature Learning. ICML Noa Rubin, Kirsten Fischer, Javed Lindner, Inbar Seroussi, Zohar Ringel, Michael Krämer, Moritz Helias
2025	J	`jnl`	From Kernels to Features: A Multi-Scale Adaptive Theory of Feature Learning. CoRR Noa Rubin, Kirsten Fischer, Javed Lindner, David Dahmen, Inbar Seroussi, Zohar Ringel, Michael Krämer, Moritz Helias
2025	J	`jnl`	Mitigating the Curse of Detail: Scaling Arguments for Feature Learning and Sample Complexity. CoRR Noa Rubin, Orit Davidovich, Zohar Ringel
2025	J	`jnl`	Wilsonian renormalization of neural network Gaussian processes Mach. Learn. Sci. Technol. Jessica N. Howard, Ro Jefferson, Anindita Maiti, Zohar Ringel
2024	A*	`conf`	Critical feature learning in deep neural networks. ICML Kirsten Fischer, Javed Lindner, David Dahmen, Zohar Ringel, Michael Krämer, Moritz Helias
2024	A*	`conf`	Grokking as a First Order Phase Transition in Two Layer Networks. ICLR Noa Rubin, Inbar Seroussi, Zohar Ringel
2024	J	`jnl`	Spectral-bias and kernel-task alignment in physically informed neural networks. Mach. Learn. Sci. Technol. Inbar Seroussi, Asaf Miron, Zohar Ringel
2024	J	`jnl`	Symmetric Kernels with Non-Symmetric Data: A Data-Agnostic Learnability Bound. CoRR Itay Lavie, Zohar Ringel
2024	A*	`conf`	Towards Understanding Inductive Bias in Transformers: A View From Infinity. ICML Itay Lavie, Guy Gur-Ari, Zohar Ringel
2024	J	`jnl`	Towards Understanding Inductive Bias in Transformers: A View From Infinity. CoRR Itay Lavie, Guy Gur-Ari, Zohar Ringel
2024	J	`jnl`	Wilsonian Renormalization of Neural Network Gaussian Processes. CoRR Jessica N. Howard, Ro Jefferson, Anindita Maiti, Zohar Ringel
2023	J	`jnl`	Droplets of Good Representations: Grokking as a First Order Phase Transition in Two Layer Networks. CoRR Noa Rubin, Inbar Seroussi, Zohar Ringel
2023	J	`jnl`	Spectral-Bias and Kernel-Task Alignment in Physically Informed Neural Networks. CoRR Inbar Seroussi, Asaf Miron, Zohar Ringel
2023	J	`jnl`	Speed Limits for Deep Learning. CoRR Inbar Seroussi, Alexander A. Alemi, Moritz Helias, Zohar Ringel
2021	A*	`conf`	A self consistent theory of Gaussian Processes captures feature learning effects in finite CNNs. NeurIPS Gadi Naveh, Zohar Ringel
2021	J	`jnl`	A self consistent theory of Gaussian Processes captures feature learning effects in finite CNNs. CoRR Gadi Naveh, Zohar Ringel
2021	J	`jnl`	Separation of scales and a thermodynamic description of feature learning in some CNNs. CoRR Inbar Seroussi, Zohar Ringel
2020	J	`jnl`	Predicting the outputs of finite networks trained with noisy gradients. CoRR Gadi Naveh, Oded Ben-David, Haim Sompolinsky, Zohar Ringel
2020	J	`jnl`	Relevance in the Renormalization Group and in Information Theory. CoRR Amit Gordon, Aditya Banerjee, Maciej Koch-Janusz, Zohar Ringel
2019	J	`jnl`	Learning Curves for Deep Neural Networks: A Gaussian Field Theory Perspective. CoRR Omry Cohen, Or Malka, Zohar Ringel
2019	J	`jnl`	The role of a layer in deep neural networks: a Gaussian Process perspective. CoRR Oded Ben-David, Zohar Ringel
2018	—	`conf`	Critical Percolation as a Framework to Analyze the Training of Deep Networks. ICLR (Poster) Zohar Ringel, Rodrigo Andrade de Bem
2017	J	`jnl`	Mutual Information, Neural Networks and the Renormalization Group. CoRR Maciej Koch-Janusz, Zohar Ringel