Diverse Feature Visualizations Reveal Invariances in Early Layers of Deep Neural Networks. - Santiago A. Cadena; Marissa A. Weis; Leon A. Gatys; Matthias Bethge; Alexander S. Ecker (ECCV (12), 2018)

Flexibly Fair Representation Learning by Disentanglement. - Elliot Creager; David Madras; Jörn-Henrik Jacobsen; Marissa A. Weis; Kevin Swersky; Toniann Pitassi; Richard S. Zemel (ICML, 2019)

Unmasking the Inductive Biases of Unsupervised Object Representations for Video Sequences. - Marissa A. Weis; Kashyap Chitta; Yash Sharma 001; Wieland Brendel; Matthias Bethge; Andreas Geiger 001; Alexander S. Ecker (CoRR, 2020)

Self-supervised Representation Learning of Neuronal Morphologies. - Marissa A. Weis; Laura Pede; Timo Lüddecke; Alexander S. Ecker (CoRR, 2021)

Benchmarking Unsupervised Object Representations for Video Sequences. - Marissa A. Weis; Kashyap Chitta; Yash Sharma 001; Wieland Brendel; Matthias Bethge; Andreas Geiger 001; Alexander S. Ecker (J. Mach. Learn. Res., 2021)

Self-Supervised Graph Representation Learning for Neuronal Morphologies. - Marissa A. Weis; Laura Pede; Timo Lüddecke; Alexander S. Ecker (Trans. Mach. Learn. Res., 2023)

MNIST-Nd: a set of naturalistic datasets to benchmark clustering across dimensions. - Polina Turishcheva; Laura Hansel; Martin Ritzert; Marissa A. Weis; Alexander S. Ecker (CoRR, 2024)

Unsupervised Representation Learning for Object-Centric and Neuronal Morphology Modeling - Marissa A. Weis (2024)

Hierarchical clustering with maximum density paths and mixture models. - Martin Ritzert; Polina Turishcheva; Laura Hansel; Paul Wollenhaupt; Marissa A. Weis; Alexander S. Ecker (CoRR, 2025)