Efficient Verification of Program Fragments: Eager POR. - Patrick Metzler; Habib Saissi; Péter Bokor; Robin Hesse; Neeraj Suri (ATVA, 2016)

Fast Axiomatic Attribution for Neural Networks. - Robin Hesse; Simone Schaub-Meyer; Stefan Roth 001 (NeurIPS, 2021)

Content-Adaptive Downsampling in Convolutional Neural Networks. - Robin Hesse; Simone Schaub-Meyer; Stefan Roth 001 (CVPR Workshops, 2023)

FunnyBirds: A Synthetic Vision Dataset for a Part-Based Analysis of Explainable AI Methods. - Robin Hesse; Simone Schaub-Meyer; Stefan Roth 001 (ICCV, 2023)

Benchmarking the Attribution Quality of Vision Models. - Robin Hesse; Simone Schaub-Meyer; Stefan Roth 001 (NeurIPS, 2024)

Disentangling Polysemantic Channels in Convolutional Neural Networks. - Robin Hesse; Jonas Fischer; Simone Schaub-Meyer; Stefan Roth 001 (CVPR Workshops, 2025)

Continual Learning Should Move Beyond Incremental Classification. - Rupert Mitchell; Antonio Alliegro; Raffaello Camoriano; Dustin Carrión-Ojeda; Antonio Carta; Georgia Chalvatzaki; Nikhil Churamani; Carlo D'Eramo; Samin Hamidi; Robin Hesse; Fabian Hinder; Roshni Kamath; et al. (CoRR, 2025)

Beyond Accuracy: What Matters in Designing Well-Behaved Models? - Robin Hesse; Dogukan Bagci; Bernt Schiele; Simone Schaub-Meyer; Stefan Roth 001 (CoRR, 2025)