| wav2vec: Unsupervised Pre-training for Speech Recognition S Schneider, A Baevski, R Collobert, M Auli Interspeech 2019, 2019 | 505 | 2019 |
| vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations A Baevski*, S Schneider*, M Auli International Conference on Learning Representations (ICLR), 2019 | 276 | 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 | 101 | 2020 |
| 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 | 60 | 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 | 50 | 2021 |
| 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 | 29 | 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 | 28 | 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 | 24 | 2017 |
| 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 | 23 | 2019 |
| 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 | 4 | 2018 |
| Multi-Task Generalization and Adaptation between Noisy Digit Datasets: An Empirical Study S Schneider, AS Ecker, JH Macke, M Bethge NeurIPS 2018 Continual Learning Workshop, 2018 | 4 | 2018 |
| 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, 1-9, 2022 | 2 | 2022 |
| Adapting ImageNet-scale models to complex distribution shifts with self-learning E Rusak*, S Schneider*, P Gehler, O Bringmann, W Brendel, M Bethge arXiv preprint arXiv:2104.12928, 2021 | 2 | 2021 |
| Towards Desynchronization Detection in Biosignals A Supratak, S Schneider, H Dong, L Li, Y Guo NIPS Time Series Workshop 2017, 2017 | 2 | 2017 |
| Unsupervised Object Learning via Common Fate M Tangemann, S Schneider, J von Kügelgen, F Locatello, P Gehler, ... arXiv preprint arXiv:2110.06562, 2021 | 1 | 2021 |
| Out-of-distribution generalization of internal models is correlated with reward KS Mann, S Schneider, A Chiappa, JH Lee, M Bethge, A Mathis, ... Self-Supervision for Reinforcement Learning Workshop-ICLR 2021, 2021 | 1 | 2021 |
| Learnable latent embeddings for joint behavioral and neural analysis S Schneider*, JH Lee*, MW Mathis arXiv preprint arXiv:2204.00673, 2022 | | 2022 |
| If your data distribution shifts, use self-learning E Rusak, S Schneider, G Pachitariu, L Eck, PV Gehler, O Bringmann, ... | | 2021 |
| Motor control: Neural correlates of optimal feedback control theory MW Mathis, S Schneider Current Biology 31 (7), R356-R358, 2021 | | 2021 |
| Generalized Invariant Risk Minimization: relating adaptation and invariant representation learning S Schneider*, S Krishna*, L Eck, W Brendel, MW Mathis, M Bethge NeurIPS 2020 Preregistration Workshop, 2020 | | 2020 |
Matthias BethgeTübingen University & EPFL Visiting ProfessorVerified email at bethgelab.org
