| Sub-sampled cubic regularization for non-convex optimization JM Kohler, A Lucchi ICML 2017, 2017 | 111 | 2017 |
| 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 | 93* | 2019 |
| Escaping Saddles with Stochastic Gradients H Daneshmand, J Kohler, A Lucchi, T Hofmann ICML 2018, 2018 | 88 | 2018 |
| The Role of Memory in Stochastic Optimization A Orvieto, J Kohler, A Lucchi UAI, 2019, 2019 | 23* | 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 | 15* | 2020 |
| 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 | 10* | 2019 |
| 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 | 9 | 2021 |
| A stochastic tensor method for non-convex optimization A Lucchi, J Kohler arXiv preprint arXiv:1911.10367, 2019 | 8 | 2019 |
| Learning Generative Models of Textured 3D Meshes from Real-World Images D Pavllo, J Kohler, T Hofmann, A Lucchi ICCV 2021, 2021 | 1 | 2021 |
| Two-Level K-FAC Preconditioning for Deep Learning N Tselepidis, J Kohler, A Orvieto NeurIPS 2020 Workshop on Optimization for Machine Learning (OPT2020), 2020 | 1 | 2020 |
| Vanishing Curvature in Randomly Initialized Deep ReLU Networks A Orvieto, J Kohler, D Pavllo, T Hofmann, A Lucchi International Conference on Artificial Intelligence and Statistics, 7942-7975, 2022 | | 2022 |
| Synthesizing Speech from Intracranial Depth Electrodes using an Encoder-Decoder Framework J Kohler, MC Ottenhoff, S Goulis, M Angrick, AJ Colon, L Wagner, ... arXiv preprint arXiv:2111.01457, 2021 | | 2021 |
| 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 | | 2021 |
| Vanishing Curvature and the Power of Adaptive Methods in Randomly Initialized Deep Networks A Orvieto, J Kohler, D Pavllo, T Hofmann, A Lucchi AISTATS 2022, 2021 | | 2021 |
Aurelien LucchiETH ZurichDirección de correo verificada de inf.ethz.ch
