| wav2vec: Unsupervised Pre-training for Speech Recognition S Schneider, A Baevski, R Collobert, M Auli Interspeech 2019, 2019 | 2142 | 2019 |
| vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations A Baevski*, S Schneider*, M Auli International Conference on Learning Representations (ICLR), 2019 | 881 | 2019 |
| Improving robustness against common corruptions by covariate shift adaptation S Schneider*, E Rusak*, L Eck, O Bringmann, W Brendel, M Bethge Advances in Neural Information Processing Systems 34, 2020 | 723 | 2020 |
| Multi-animal pose estimation, identification and tracking with DeepLabCut J Lauer, M Zhou, S Ye, W Menegas, S Schneider, T Nath, MM Rahman, ... Nature Methods 19 (4), 496-504, 2022 | 608 | 2022 |
| Learnable latent embeddings for joint behavioral and neural analysis S Schneider*, JH Lee*, MW Mathis Nature 617, 360–368, 2023 | 434 | 2023 |
| Contrastive Learning Inverts the Data Generating Process RS Zimmermann*, Y Sharma*, S Schneider*, M Bethge, W Brendel 38th International Conference on Machine Learning (ICML), 2021 | 320 | 2021 |
| A Primer on Motion Capture with Deep Learning: Principles, Pitfalls, and Perspectives A Mathis, S Schneider, J Lauer, MW Mathis Neuron 108 (1), 44-65, 2020 | 280 | 2020 |
| Pretraining boosts out-of-domain robustness for pose estimation A Mathis*, T Biasi*, S Schneider, M Yüksekgönül, B Rogers, M Bethge, ... Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2021 | 178 | 2021 |
| SuperAnimal pretrained pose estimation models for behavioral analysis S Ye, A Filippova, J Lauer, S Schneider, M Vidal, T Qiu, A Mathis, ... Nature Communications 15 (1), 5165, 2024 | 111* | 2024 |
| Rdumb: A simple approach that questions our progress in continual test-time adaptation O Press, S Schneider, M Kümmerer, M Bethge Advances in Neural Information Processing Systems 36, 39915-39935, 2023 | 61 | 2023 |
| Iron-sequestering nanocompartments as multiplexed Electron Microscopy gene reporters F Sigmund, S Pettinger, M Kube, F Schneider, M Schifferer, S Schneider, ... ACS nano 13 (7), 8114-8123, 2019 | 55 | 2019 |
| If your data distribution shifts, use self-learning E Rusak*, S Schneider*, G Pachitariu, L Eck, PV Gehler, O Bringmann, ... Transactions of Machine Learning Research (TMLR), 2021 | 49* | 2021 |
| Context-based Normalization of Histological Stains using Deep Convolutional Features D Bug*, S Schneider*, A Grote, E Oswald, F Feuerhake, J Schüler, ... Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical …, 2017 | 48 | 2017 |
| NeuBtracker — imaging neurobehavioral dynamics in freely behaving fish P Symvoulidis, A Lauri, A Stefanoiu, M Cappetta, S Schneider, H Jia, ... Nature Methods 14 (11), 1079-1082, 2017 | 43 | 2017 |
| Unsupervised Object Learning via Common Fate M Tangemann, S Schneider, J von Kügelgen, F Locatello, P Gehler, ... 2nd conference on Causal Learning and Reasoning (CLeaR) 2023, 2021 | 32 | 2021 |
| Sparse autoencoders reveal selective remapping of visual concepts during adaptation H Lim, J Choi, J Choo, S Schneider The Thirteenth International Conference on Learning Representations, 2024 | 20 | 2024 |
| Neuro-musculoskeletal modeling reveals muscle-level neural dynamics of adaptive learning in sensorimotor cortex T DeWolf*, S Schneider*, P Soubiran, A Roggenbach, M Mathis bioRxiv, 2024.09. 11.612513, 2024 | 15 | 2024 |
| ImageNet-D: A new challenging robustness dataset inspired by domain adaptation E Rusak*, S Schneider*, PV Gehler, O Bringmann, W Brendel, M Bethge ICML 2022 Shift Happens Workshop, 2022 | 15 | 2022 |
| Motor control: Neural correlates of optimal feedback control theory MW Mathis, S Schneider Current Biology 31 (7), R356-R358, 2021 | 15 | 2021 |
| Salad: A Toolbox for Semi-supervised Adaptive Learning Across Domains S Schneider, AS Ecker, JH Macke, M Bethge NeurIPS 2018 Machine Learning Open Source Software Workshop, 2018 | 8 | 2018 |
Mackenzie Weygandt MathisSwiss Federal Institute of Technology in Lausanne (EPFL)Verified email at epfl.ch
