| Sub-sampled cubic regularization for non-convex optimization JM Kohler, A Lucchi ICML 2017, 2017 | 186 | 2017 |
| Escaping Saddles with Stochastic Gradients H Daneshmand, J Kohler, A Lucchi, T Hofmann ICML 2018, 2018 | 166 | 2018 |
| Exponential convergence rates for Batch Normalization: The power of length-direction decoupling in non-convex optimization J Kohler, H Daneshmand, A Lucchi, M Zhou, K Neymeyr, T Hofmann AISTATS 2019, 2019 | 151* | 2019 |
| Batch normalization provably avoids ranks collapse for randomly initialised deep networks H Daneshmand, J Kohler, F Bach, T Hofmann, A Lucchi NeurIPS 2020, 2020 | 64 | 2020 |
| This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networks A Hoffmann, C Fanconi, R Rade, J Kohler ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend …, 2021 | 54 | 2021 |
| Learning Generative Models of Textured 3D Meshes from Real-World Images D Pavllo, J Kohler, T Hofmann, A Lucchi ICCV 2021, 2021 | 54 | 2021 |
| The Role of Memory in Stochastic Optimization A Orvieto, J Kohler, A Lucchi UAI, 2019, 2019 | 41* | 2019 |
| Synthesizing Speech from Intracranial Depth Electrodes using an Encoder-Decoder Framework J Kohler, MC Ottenhoff, S Goulis, M Angrick, AJ Colon, L Wagner, ... Neurons, Behavior, Data analysis, and Theory (NBDT), 2021 | 24 | 2021 |
| Adaptive norms for deep learning with regularised Newton methods J Kohler, L Adolphs, A Lucchi NeurIPS 2019 Workshop: Beyond First-Order Optimization Methods in Machine …, 2019 | 19* | 2019 |
| Vanishing Curvature in Randomly Initialized Deep ReLU Networks. A Orvieto, J Kohler, D Pavllo, T Hofmann, A Lucchi AISTATS, 7942-7975, 2022 | 16* | 2022 |
| Safe Deep Reinforcement Learning for Multi-Agent Systems with Continuous Action Spaces Z Sheebaelhamd, K Zisis, A Nisioti, D Gkouletsos, D Pavllo, J Kohler ICML 2021 Workshop on Reinforcement Learning for Real Life Workshop, 2021 | 16 | 2021 |
| A Sub-sampled Tensor Method for Non-convex Optimization A Lucchi, J Kohler IMA Journal of Numerical Analysis 43 (5), 2019 | 16* | 2019 |
| Imagine flash: Accelerating emu diffusion models with backward distillation J Kohler, A Pumarola, E Schönfeld, A Sanakoyeu, R Sumbaly, P Vajda, ... arXiv preprint arXiv:2405.05224, 2024 | 10 | 2024 |
| Cache Me if You Can: Accelerating Diffusion Models through Block Caching F Wimbauer, B Wu, E Schoenfeld, X Dai, J Hou, Z He, A Sanakoyeu, ... CVPR 2024, 2023 | 10 | 2023 |
| Two-Level K-FAC Preconditioning for Deep Learning N Tselepidis, J Kohler, A Orvieto NeurIPS 2020 Workshop on Optimization for Machine Learning (OPT2020), 2020 | 7 | 2020 |
| Adaptive guidance: Training-free acceleration of conditional diffusion models A Castillo, J Kohler, JC Pérez, JP Pérez, A Pumarola, B Ghanem, ... arXiv preprint arXiv:2312.12487, 2023 | 5 | 2023 |
| fMPI: Fast Novel View Synthesis in the Wild with Layered Scene Representations J Kohler, NG Sanchez, L Cavalli, C Herold, A Pumarola, AG Garcia, ... CV4MR 2024, 2023 | 1 | 2023 |
| Insights on the interplay of network architectures and optimization algorithms in deep learning J Kohler ETH Zurich, 2022 | | 2022 |
Aurelien LucchiAssistant Professor, Department of Mathematics and Computer Science, University of BaselBestätigte E-Mail-Adresse bei inf.ethz.ch
