Simion Zoo: A Workbench for Distributed Experimentation with Reinforcement Learning for Continuous Control Tasks. - Borja Fernández-Gauna; Manuel Graña; Roland S. Zimmermann (CoRR, 2019)

Comment on "Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network". - Roland S. Zimmermann (CoRR, 2019)

Faster training of Mask R-CNN by focusing on instance boundaries. - Roland S. Zimmermann; Julien N. Siems (Comput. Vis. Image Underst., 2019)

A Self-Supervised Feature Map Augmentation (FMA) Loss and Combined Augmentations Finetuning to Efficiently Improve the Robustness of CNNs. - Nikhil Kapoor; Chun Yuan; Jonas Löhdefink; Roland Zimmermann 002; Serin Varghese; Fabian Hüger; Nico M. Schmidt; Peter Schlicht; Tim Fingscheidt (CSCS, 2020)

A Simple Way to Make Neural Networks Robust Against Diverse Image Corruptions. - Evgenia Rusak; Lukas Schott; Roland S. Zimmermann; Julian Bitterwolf; Oliver Bringmann 001; Matthias Bethge; Wieland Brendel (ECCV (3), 2020)

Increasing the robustness of DNNs against image corruptions by playing the Game of Noise. - Evgenia Rusak; Lukas Schott; Roland S. Zimmermann; Julian Bitterwolf; Oliver Bringmann 001; Matthias Bethge; Wieland Brendel (CoRR, 2020)

Exemplary Natural Images Explain CNN Activations Better than Feature Visualizations. - Judy Borowski; Roland S. Zimmermann; Judith Schepers; Robert Geirhos; Thomas S. A. Wallis; Matthias Bethge; Wieland Brendel (CoRR, 2020)

Reconstructing Complex Cardiac Excitation Waves From Incomplete Data Using Echo State Networks and Convolutional Autoencoders. - Sebastian Herzog; Roland S. Zimmermann; Johannes Abele; Stefan Luther; Ulrich Parlitz (Frontiers Appl. Math. Stat., 2020)

Foolbox Native: Fast adversarial attacks to benchmark the robustness of machine learning models in PyTorch, TensorFlow, and JAX. - Jonas Rauber; Roland Zimmermann 002; Matthias Bethge; Wieland Brendel (J. Open Source Softw., 2020)

Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization. - Judy Borowski; Roland Simon Zimmermann; Judith Schepers; Robert Geirhos; Thomas S. A. Wallis; Matthias Bethge; Wieland Brendel (ICLR, 2021)

Contrastive Learning Inverts the Data Generating Process. - Roland S. Zimmermann; Yash Sharma 001; Steffen Schneider 001; Matthias Bethge; Wieland Brendel (ICML, 2021)

How Well do Feature Visualizations Support Causal Understanding of CNN Activations? - Roland S. Zimmermann; Judy Borowski; Robert Geirhos; Matthias Bethge; Thomas S. A. Wallis; Wieland Brendel (NeurIPS, 2021)

Score-Based Generative Classifiers. - Roland S. Zimmermann; Lukas Schott; Yang Song; Benjamin A. Dunn; David A. Klindt (CoRR, 2021)

Increasing Confidence in Adversarial Robustness Evaluations. - Roland S. Zimmermann; Wieland Brendel; Florian Tramèr; Nicholas Carlini (NeurIPS, 2022)

Provably Learning Object-Centric Representations. - Jack Brady; Roland S. Zimmermann; Yash Sharma 001; Bernhard Schölkopf; Julius von Kügelgen; Wieland Brendel (ICML, 2023)

Scale Alone Does not Improve Mechanistic Interpretability in Vision Models. - Roland S. Zimmermann; Thomas Klein; Wieland Brendel (NeurIPS, 2023)

Sensitivity of Slot-Based Object-Centric Models to their Number of Slots. - Roland S. Zimmermann; Sjoerd van Steenkiste; Mehdi S. M. Sajjadi; Thomas Kipf; Klaus Greff (CoRR, 2023)

Don't trust your eyes: on the (un)reliability of feature visualizations. - Robert Geirhos; Roland S. Zimmermann; Blair L. Bilodeau; Wieland Brendel; Been Kim (ICML, 2024)

Measuring Per-Unit Interpretability at Scale Without Humans. - Roland S. Zimmermann; David A. Klindt; Wieland Brendel (NeurIPS, 2024)

InfoNCE: Identifying the Gap Between Theory and Practice. - Evgenia Rusak; Patrik Reizinger; Attila Juhos; Oliver Bringmann 001; Roland S. Zimmermann; Wieland Brendel (AISTATS, 2025)

In Search of Forgotten Domain Generalization. - Prasanna Mayilvahanan; Roland S. Zimmermann; Thaddäus Wiedemer; Evgenia Rusak; Attila Juhos; Matthias Bethge; Wieland Brendel (ICLR, 2025)

Evaluating Frontier Models for Stealth and Situational Awareness. - Mary Phuong; Roland S. Zimmermann; Ziyue Wang; David Lindner; Victoria Krakovna; Sarah Cogan; Allan Dafoe; Lewis Ho; Rohin Shah (CoRR, 2025)

LAION-C: An Out-of-Distribution Benchmark for Web-Scale Vision Models. - Fanfei Li; Thomas Klein; Wieland Brendel; Robert Geirhos; Roland S. Zimmermann (CoRR, 2025)

Early Signs of Steganographic Capabilities in Frontier LLMs. - Artur Zolkowski; Kei Nishimura-Gasparian; Robert McCarthy; Roland S. Zimmermann; David Lindner (CoRR, 2025)