Dynamical Inference
Schneider LabWe build machine learning algorithms for representation learning and inference of nonlinear system dynamics. We apply these algorithms to model complex biological systems in neuroscience, cell biology and other life science applications.
We build machine learning algorithms for representation learning and inference of nonlinear system dynamics. We apply these algorithms to model complex biological systems in neuroscience, cell biology and other life science applications.
Our Researchers
Publications
Rodrigo Gonzalez Laiz, Tobias Schmidt, Steffen Schneider. Self-supervised contrastive learning performs non-linear system identification. ICLR 2025.
Hyesu Lim, Jinho Choi, Jaegul Choo, Steffen Schneider. Sparse Autoencoders Reveal Selective Remapping of Visual Concepts During Adaptation. ICLR 2025.
Steffen Schneider, Jin H Lee, Mackenzie W Mathis. Learnable Latent Embeddings for Joint Behavioral and Neural Analysis. Nature, 2023.
Roland S Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, Wieland Brendel.Contrastive Learning Inverts the Data Generating Process. ICML, 2021.
Steffen Schneider, Evgenia Rusak, Luisa Eck, Oliver Bringmann, Wieland Brendel, Matthias Bethge. Improving Robustness against Common Corruptions by Covariate Shift Adaptation. NeurIPS, 2020.
Steffen Schneider, Alexei Baevski, Ronan Collobert, Michael Auli. wav2vec: Unsupervised Pre-training for Speech Recognition. Interspeech, 2019
