M. A. Weis, L. Pede, T. Lüddecke, and A. S. Ecker
Self-supervised Representation Learning of Neuronal Morphologies
ArXiv, 2021
Code, URL, BibTex

M. A. Weis, K. Chitta, Y. Sharma, W. Brendel, M. Bethge, A. Geiger, and A. S. Ecker
Benchmarking Unsupervised Object Representations for Video Sequences
Journal of Machine Learning Research, 22, 1-61, 2021
Code, URL, BibTex

D. Kobak, Y. Bernaerts, M. A. Weis, F. Scala, A. S. Tolias, and P. Berens
Sparse reduced‐rank regression for exploratory visualisation of paired multivariate data
Journal of the Royal Statistical Society: Series C (Applied Statistics), 70, 980-1000, 2021
URL, BibTex

E. Creager, D. Madras, J.-H. Jacobsen, M. A. Weis, K. Swersky, T. Pitassi, and R. Zemel
Flexibly Fair Representation Learning by Disentanglement
International Conference on Machine Learning (ICML), 2019
URL, BibTex

S. A. Cadena, M. A. Weis, L. A. Gatys, M. Bethge, and A. S. Ecker
Diverse feature visualizations reveal invariances in early layers of deep neural networks
The European Conference on Computer Vision (ECCV), 2018
URL, BibTex