| Mathematical Capabilities of ChatGPT S Frieder, L Pinchetti, RR Griffiths, T Salvatori, T Lukasiewicz, ... Advances in Neural Information Processing Systems 36, 2024 | 336* | 2024 |
| Analysis of the Generalization Error: Empirical Risk Minimization over Deep Artificial Neural Networks Overcomes the Curse of Dimensionality in the Numerical Approximation of … J Berner, P Grohs, A Jentzen SIAM Journal on Mathematics of Data Science 2 (3), 631-657, 2020 | 192 | 2020 |
| The Modern Mathematics of Deep Learning J Berner, P Grohs, G Kutyniok, P Petersen Mathematical Aspects of Deep Learning, 1-111, 2022 | 181* | 2022 |
| Group testing for SARS-CoV-2 allows for up to 10-fold efficiency increase across realistic scenarios and testing strategies CM Verdun, T Fuchs, P Harar, D Elbrächter, DS Fischer, J Berner, ... Frontiers in Public Health 9, 583377, 2021 | 65 | 2021 |
| Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning J Berner, M Dablander, P Grohs Advances in Neural Information Processing Systems 33, 2020 | 43 | 2020 |
| How degenerate is the parametrization of neural networks with the ReLU activation function? J Berner, D Elbrächter, P Grohs Advances in Neural Information Processing Systems, 7790-7801, 2019 | 38* | 2019 |
| An optimal control perspective on diffusion-based generative modeling J Berner, L Richter, K Ullrich Transactions on Machine Learning Research, 2024 | 25 | 2024 |
| Towards a regularity theory for ReLU networks–chain rule and global error estimates J Berner, D Elbrächter, P Grohs, A Jentzen 2019 13th International conference on Sampling Theory and Applications …, 2019 | 15 | 2019 |
| Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning L Richter, J Berner International Conference on Machine Learning, 18649-18666, 2022 | 13 | 2022 |
| Improved sampling via learned diffusions L Richter, J Berner International Conference on Learning Representations, 2024 | 12 | 2024 |
| Learning ReLU networks to high uniform accuracy is intractable J Berner, P Grohs, F Voigtlaender International Conference on Learning Representations, 2023 | 6 | 2023 |
| Physics-Informed Neural Operators with Exact Differentiation on Arbitrary Geometries C White, J Berner, J Kossaifi, M Elleithy, D Pitt, D Leibovici, Z Li, ... The Symbiosis of Deep Learning and Differential Equations III, 2023 | 3 | 2023 |
| Large Language Models for Mathematicians S Frieder, J Berner, P Petersen, T Lukasiewicz International Mathematical News 254, 2023 | 2 | 2023 |
| Neural Operators with Localized Integral and Differential Kernels M Liu-Schiaffini, J Berner, B Bonev, T Kurth, K Azizzadenesheli, ... arXiv preprint arXiv:2402.16845, 2024 | 1 | 2024 |
| Pretraining Codomain Attention Neural Operators for Solving Multiphysics PDEs MA Rahman, RJ George, M Elleithy, D Leibovici, Z Li, B Bonev, C White, ... arXiv preprint arXiv:2403.12553, 2024 | | 2024 |
| DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training Z Hao, C Su, S Liu, J Berner, C Ying, H Su, A Anandkumar, J Song, J Zhu arXiv preprint arXiv:2403.03542, 2024 | | 2024 |
| Solving Poisson Equations using Neural Walk-on-Spheres HC Nam, J Berner, A Anandkumar ICLR 2024 Workshop on AI4DifferentialEquations In Science, 2024 | | 2024 |
| Physics-informed neural networks for sampling J Sun, J Berner, K Azizzadenesheli, A Anandkumar ICLR 2024 Workshop on AI4DifferentialEquations In Science, 2024 | | 2024 |
| Mathematical Analysis of Deep Learning with Applications to Kolmogorov Equations J Berner University of Vienna, 2023 | | 2023 |
| Die moderne Mathematik des tiefen Lernens J Berner, P Grohs, G Kutyniok, P Petersen Mitteilungen der Deutschen Mathematiker-Vereinigung 29 (4), 191-197, 2021 | | 2021 |
philipp grohsUniversity of ViennaVerified email at univie.ac.at
