| Mathematical Capabilities of ChatGPT S Frieder, L Pinchetti, RR Griffiths, T Salvatori, T Lukasiewicz, ... Advances in Neural Information Processing Systems 36, 2024 | 432* | 2024 |
| The Modern Mathematics of Deep Learning J Berner, P Grohs, G Kutyniok, P Petersen Mathematical Aspects of Deep Learning, 1-111, 2022 | 233* | 2022 |
| 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 | 206 | 2020 |
| 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 | 66 | 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 | 55 | 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 | 41* | 2019 |
| An optimal control perspective on diffusion-based generative modeling J Berner, L Richter, K Ullrich Transactions on Machine Learning Research, 2024 | 30 | 2024 |
| Improved sampling via learned diffusions L Richter, J Berner International Conference on Learning Representations, 2024 | 18 | 2024 |
| Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning L Richter, J Berner International Conference on Machine Learning, 18649-18666, 2022 | 17 | 2022 |
| 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 | 16 | 2019 |
| Learning ReLU networks to high uniform accuracy is intractable J Berner, P Grohs, F Voigtlaender International Conference on Learning Representations, 2023 | 11 | 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 | 6 | 2023 |
| 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 | 5 | 2024 |
| Large Language Models for Mathematicians S Frieder, J Berner, P Petersen, T Lukasiewicz International Mathematical News 254, 2023 | 5 | 2023 |
| 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 | 4 | 2024 |
| 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 | 4 | 2024 |
| Solving Poisson Equations using Neural Walk-on-Spheres HC Nam, J Berner, A Anandkumar Forty-first International Conference on Machine Learning, 2024 | 1 | 2024 |
| Dynamical Measure Transport and Neural PDE Solvers for Sampling J Sun, J Berner, L Richter, M Zeinhofer, J Müller, K Azizzadenesheli, ... arXiv preprint arXiv:2407.07873, 2024 | | 2024 |
| Reduced-Order Neural Operators: Learning Lagrangian Dynamics on Highly Sparse Graphs H Viswanath, Y Chang, J Berner, PY Chen, A Bera arXiv preprint arXiv:2407.03925, 2024 | | 2024 |
| Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing B Zhang, W Chu, J Berner, C Meng, A Anandkumar, Y Song arXiv preprint arXiv:2407.01521, 2024 | | 2024 |
philipp grohsUniversity of ViennaVerified email at univie.ac.at
