| Neural tangent kernel: Convergence and generalization in neural networks A Jacot, F Gabriel, C Hongler Advances in neural information processing systems 31, 2018 | 3690 | 2018 |
| Scaling description of generalization with number of parameters in deep learning M Geiger, A Jacot, S Spigler, F Gabriel, L Sagun, S d’Ascoli, G Biroli, ... Journal of Statistical Mechanics: Theory and Experiment 2020 (2), 023401, 2020 | 232 | 2020 |
| Convergence of Ising interfaces to Schrammʼs SLE curves D Chelkak, H Duminil-Copin, C Hongler, A Kemppainen, S Smirnov Comptes Rendus. Mathématique 352 (2), 157-161, 2014 | 219 | 2014 |
| Conformal invariance of spin correlations in the planar Ising model D Chelkak, C Hongler, K Izyurov Annals of Mathematics, Pages 1087-1138 from Volume 181, Issue 3, 2015 | 190 | 2015 |
| Connection probabilities and RSW‐type bounds for the two‐dimensional FK Ising model H Duminil‐Copin, C Hongler, P Nolin Communications on pure and applied mathematics 64 (9), 1165-1198, 2011 | 123* | 2011 |
| The energy density in the planar Ising model C Hongler, S Smirnov Acta Mathematica, Issue 2, pp 191–225, 2013 | 121* | 2013 |
| Implicit regularization of random feature models A Jacot, B Simsek, F Spadaro, C Hongler, F Gabriel International Conference on Machine Learning, 4631-4640, 2020 | 106 | 2020 |
| Geometry of the loss landscape in overparameterized neural networks: Symmetries and invariances B Simsek, F Ged, A Jacot, F Spadaro, C Hongler, W Gerstner, J Brea International Conference on Machine Learning, 9722-9732, 2021 | 92 | 2021 |
| The scaling limit of critical Ising interfaces is S Benoist, C Hongler | 81 | 2019 |
| Crossing probabilities in topological rectangles for the critical planar FK-Ising model D Chelkak, H Duminil-Copin, C Hongler | 71 | 2016 |
| Conformal invariance of Ising model correlations C Hongler XVIIth International Congress on Mathematical Physics, 326-335, 2013 | 69 | 2013 |
| Ising interfaces and free boundary conditions C Hongler, K Kytölä Journal of the American Mathematical Society 26 (4), 1107-1189, 2013 | 69* | 2013 |
| Kernel alignment risk estimator: Risk prediction from training data A Jacot, B Simsek, F Spadaro, C Hongler, F Gabriel Advances in neural information processing systems 33, 15568-15578, 2020 | 65 | 2020 |
| Saddle-to-saddle dynamics in deep linear networks: Small initialization training, symmetry, and sparsity A Jacot, F Ged, B Şimşek, C Hongler, F Gabriel arXiv preprint arXiv:2106.15933, 2021 | 49 | 2021 |
| The asymptotic spectrum of the hessian of dnn throughout training A Jacot, F Gabriel, C Hongler arXiv preprint arXiv:1910.02875, 2019 | 32 | 2019 |
| Conformal invariance of crossing probabilities for the Ising model with free boundary conditions S Benoist, H Duminil-Copin, C Hongler | 24 | 2016 |
| Correlations of primary fields in the critical Ising model D Chelkak, C Hongler, K Izyurov arXiv preprint arXiv:2103.10263, 2021 | 23 | 2021 |
| Ising Model: Local Spin Correlations and Conformal Invariance R Gheissari, C Hongler, S Park arXiv preprint arXiv:1312.4446, 2016 | 23 | 2016 |
| Critical percolation: the expected number of clusters in a rectangle C Hongler, S Smirnov Probability theory and related fields 151, 735-756, 2011 | 21 | 2011 |
| Freeze and chaos for dnns: an ntk view of batch normalization, checkerboard and boundary effects A Jacot, F Gabriel, C Hongler arXiv preprint arXiv:1907.05715 6, 2019 | 19 | 2019 |
Dmitry ChelkakUniversity of MichiganVerified email at umich.edu
