| On the rate of convergence of fully connected deep neural network regression estimates M Kohler, S Langer The Annals of Statistics 49 (4), 2231-2249, 2021 | 192* | 2021 |
| Approximating smooth functions by deep neural networks with sigmoid activation function S Langer Journal of Multivariate Analysis 182, 104696, 2021 | 83 | 2021 |
| Estimation of a function of low local dimensionality by deep neural networks M Kohler, A Krzyżak, S Langer IEEE transactions on information theory 68 (6), 4032-4042, 2022 | 50 | 2022 |
| Analysis of the rate of convergence of fully connected deep neural network regression estimates with smooth activation function S Langer Journal of Multivariate Analysis 182, 104695, 2021 | 28 | 2021 |
| Statistical theory for image classification using deep convolutional neural networks with cross-entropy loss under the hierarchical max-pooling model M Kohler, S Langer arXiv preprint arXiv:2011.13602, 2020 | 21 | 2020 |
| Discussion of:“Nonparametric regression using deep neural networks with ReLU activation function” M Kohler, S Langer | 16 | 2020 |
| The smoking gun: Statistical theory improves neural network estimates A Braun, M Kohler, S Langer, H Walk | 13 | 2021 |
| Convergence rates for shallow neural networks learned by gradient descent A Braun, M Kohler, S Langer, H Walk Bernoulli 30 (1), 475-502, 2024 | 10 | 2024 |
| Statistical theory for image classification using deep convolutional neural network with cross-entropy loss under the hierarchical max-pooling model M Kohler, S Langer Journal of Statistical Planning and Inference 234, 106188, 2025 | 7 | 2025 |
| Estimation of a regression function on a manifold by fully connected deep neural networks M Kohler, S Langer, U Reif Journal of Statistical Planning and Inference 222, 160-181, 2023 | 7 | 2023 |
| Dropout Regularization Versus l2-Penalization in the Linear Model G Clara, S Langer, J Schmidt-Hieber Journal of Machine Learning Research 25 (204), 1-48, 2024 | 6 | 2024 |
| A statistical analysis of an image classification problem S Langer, J Schmidt-Hieber arXiv e-prints, arXiv: 2206.02151, 2022 | 5 | 2022 |
| Learning Green's Function Efficiently Using Low-Rank Approximations K Wimalawarne, T Suzuki, S Langer arXiv preprint arXiv:2308.00350, 2023 | 2 | 2023 |
| Supplement to “On the rate of convergence of fully connected deep neural network regression estimates.” M Kohler, S Langer | 2 | 2021 |
| WITHDRAWN: Approximation properties of fully connected deep neural networks M Kohler, S Langer Journal of Multivariate Analysis, 2019 | 2 | 2019 |
| On the VC dimension of deep group convolutional neural networks A Sepliarskaia, S Langer, J Schmidt-Hieber arXiv preprint arXiv:2410.15800, 2024 | | 2024 |
| Accelerated mirror descent for non-Euclidean star-convex functions C Lezane, S Langer, WM Koolen arXiv preprint arXiv:2405.18976, 2024 | | 2024 |
| A novel statistical approach to analyze image classification J Chen, S Langer, J Schmidt-Hieber arXiv preprint arXiv:2206.02151, 2022 | | 2022 |
| The Smoking Gun: Statistical Theory Improves Neural Network Estimates ABM Kohler, S Langer, H Walk | | 2021 |
| Ein Beitrag zur statistischen Theorie des Deep Learnings S Langer | | 2020 |
