| Topological superconducting phases of weakly coupled quantum wires I Seroussi, E Berg, Y Oreg Physical Review B 89 (10), 104523, 2014 | 85 | 2014 |
| Separation of scales and a thermodynamic description of feature learning in some cnns I Seroussi, G Naveh, Z Ringel Nature Communications 14 (1), 908, 2023 | 71 | 2023 |
| Segmentation and tracking of live cells in phase‐contrast images using directional gradient vector flow for snakes I Seroussi, D Veikherman, N Ofer, S Yehudai‐Resheff, K Keren Journal of microscopy 247 (2), 137-146, 2012 | 69 | 2012 |
| Hitting the high-dimensional notes: An ode for sgd learning dynamics on glms and multi-index models E Collins-Woodfin, C Paquette, E Paquette, I Seroussi Information and Inference: A Journal of the IMA 13 (4), iaae028, 2024 | 20 | 2024 |
| Grokking as a first order phase transition in two layer networks N Rubin, I Seroussi, Z Ringel arXiv preprint arXiv:2310.03789, 2023 | 13 | 2023 |
| Single‐and double‐Diffusion encoding MRI for studying ex vivo apparent axon diameter distribution in spinal cord white matter D Anaby, D Morozov, I Seroussi, S Hametner, N Sochen, Y Cohen NMR in Biomedicine 32 (12), e4170, 2019 | 12 | 2019 |
| Microscopic interpretation and generalization of the Bloch-Torrey equation for diffusion magnetic resonance I Seroussi, DS Grebenkov, O Pasternak, N Sochen Journal of Magnetic Resonance 277, 95-103, 2017 | 12 | 2017 |
| Directed polymers on infinite graphs C Cosco, I Seroussi, O Zeitouni Communications in mathematical physics 386 (1), 395-432, 2021 | 10 | 2021 |
| Lower bounds on the generalization error of nonlinear learning models I Seroussi, O Zeitouni IEEE transactions on information theory 68 (12), 7956-7970, 2022 | 8 | 2022 |
| Spectral analysis of a non-equilibrium stochastic dynamics on a general network I Seroussi, N Sochen Scientific reports 8 (1), 14333, 2018 | 7 | 2018 |
| Multi-Season Analysis Reveals the Spatial Structure of Disease Spread I Seroussi, N Levy, E Yom-Tov arXiv preprint arXiv:1902.04073, 2019 | 6 | 2019 |
| The high line: Exact risk and learning rate curves of stochastic adaptive learning rate algorithms E Collins-Woodfin, I Seroussi, B García Malaxechebarría, AW Mackenzie, ... Advances in Neural Information Processing Systems 37, 6500-6548, 2025 | 3 | 2025 |
| A Unified Approach to Feature Learning in Bayesian Neural Networks N Rubin, Z Ringel, I Seroussi, M Helias High-dimensional Learning Dynamics 2024: The Emergence of Structure and …, 2024 | 2 | 2024 |
| Optimal minimax rate of learning interaction kernels X Wang, I Seroussi, F Lu arXiv preprint arXiv:2311.16852, 2023 | 2 | 2023 |
| From Kernels to Features: A Multi-Scale Adaptive Theory of Feature Learning N Rubin, K Fischer, J Lindner, D Dahmen, I Seroussi, Z Ringel, M Krämer, ... arXiv preprint arXiv:2502.03210, 2025 | 1 | 2025 |
| Spectral-bias and kernel-task alignment in physically informed neural networks I Seroussi, A Miron, Z Ringel Machine Learning: Science and Technology 5 (3), 035048, 2024 | 1 | 2024 |
| On the use of multiple compartment epidemiological models to describe the dynamics of influenza in Europe I Seroussi, N Levy, D Paolotti, N Sochen, E Yom-Tov arXiv preprint arXiv:1906.08631, 2019 | 1 | 2019 |
| Bloch-Torrey simulation to quantify diffusion effects on multi-parametric sequences with application for MR fingerprinting I Seroussi, N Sochen, N Ben-Eliezer, O Pasternak Proceedings of the Joint Annual Meeting ISMRM-ESMRMB, 4267, 2018 | 1 | 2018 |
| Applications of Statistical Field Theory in Deep Learning Z Ringel, N Rubin, E Mor, M Helias, I Seroussi arXiv preprint arXiv:2502.18553, 2025 | | 2025 |
| Speed Limits for Deep Learning I Seroussi, AA Alemi, M Helias, Z Ringel arXiv preprint arXiv:2307.14653, 2023 | | 2023 |
