Structured Receptive Fields in CNNs. - Jörn-Henrik Jacobsen; Jan C. van Gemert; Zhongyu Lou; Arnold W. M. Smeulders (CVPR, 2016)

Dynamic Steerable Blocks in Deep Residual Networks. - Jörn-Henrik Jacobsen; Bert De Brabandere; Arnold W. M. Smeulders (BMVC, 2017)

Multiscale Hierarchical Convolutional Networks. - Jörn-Henrik Jacobsen; Edouard Oyallon; Stéphane Mallat; Arnold W. M. Smeulders (CoRR, 2017)

i-RevNet: Deep Invertible Networks. - Jörn-Henrik Jacobsen; Arnold W. M. Smeulders; Edouard Oyallon (ICLR (Poster), 2018)

Invertible Residual Networks. - Jens Behrmann; David Duvenaud; Jörn-Henrik Jacobsen (CoRR, 2018)

Excessive Invariance Causes Adversarial Vulnerability. - Jörn-Henrik Jacobsen; Jens Behrmann; Richard S. Zemel; Matthias Bethge (ICLR (Poster), 2019)

Invertible Residual Networks. - Jens Behrmann; Will Grathwohl; Ricky T. Q. Chen; David Duvenaud; Jörn-Henrik Jacobsen (ICML, 2019)

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)

Residual Flows for Invertible Generative Modeling. - Tian Qi Chen; Jens Behrmann; David Duvenaud; Jörn-Henrik Jacobsen (NeurIPS, 2019)

Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks. - Qiyang Li; Saminul Haque; Cem Anil; James Lucas; Roger B. Grosse; Jörn-Henrik Jacobsen (NeurIPS, 2019)

Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness. - Jörn-Henrik Jacobsen; Jens Behrmann; Nicholas Carlini; Florian Tramèr; Nicolas Papernot (CoRR, 2019)

Conditional Generative Models are not Robust. - Ethan Fetaya; Jörn-Henrik Jacobsen; Richard S. Zemel (CoRR, 2019)

Residual Flows for Invertible Generative Modeling. - Ricky T. Q. Chen; Jens Behrmann; David Duvenaud; Jörn-Henrik Jacobsen (CoRR, 2019)

Understanding the Limitations of Conditional Generative Models. - Ethan Fetaya; Jörn-Henrik Jacobsen; Will Grathwohl; Richard S. Zemel (ICLR, 2020)

Your classifier is secretly an energy based model and you should treat it like one. - Will Grathwohl; Kuan-Chieh Wang; Jörn-Henrik Jacobsen; David Duvenaud; Mohammad Norouzi 002; Kevin Swersky (ICLR, 2020)

How to Train Your Neural ODE: the World of Jacobian and Kinetic Regularization. - Chris Finlay; Jörn-Henrik Jacobsen; Levon Nurbekyan; Adam M. Oberman (ICML, 2020)

Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling. - Will Grathwohl; Kuan-Chieh Wang; Jörn-Henrik Jacobsen; David Duvenaud; Richard S. Zemel (ICML, 2020)

Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations. - Florian Tramèr; Jens Behrmann; Nicholas Carlini; Nicolas Papernot; Jörn-Henrik Jacobsen (ICML, 2020)

How to train your neural ODE. - Chris Finlay; Jörn-Henrik Jacobsen; Levon Nurbekyan; Adam M. Oberman (CoRR, 2020)

Cutting out the Middle-Man: Training and Evaluating Energy-Based Models without Sampling. - Will Grathwohl; Kuan-Chieh Wang; Jörn-Henrik Jacobsen; David Duvenaud; Richard S. Zemel (CoRR, 2020)

Out-of-Distribution Generalization via Risk Extrapolation (REx). - David Krueger 001; Ethan Caballero; Jörn-Henrik Jacobsen; Amy Zhang 001; Jonathan Binas; Rémi Le Priol; Aaron C. Courville (CoRR, 2020)

Exchanging Lessons Between Algorithmic Fairness and Domain Generalization. - Elliot Creager; Jörn-Henrik Jacobsen; Richard S. Zemel (CoRR, 2020)

Shortcut learning in deep neural networks. - Robert Geirhos; Jörn-Henrik Jacobsen; Claudio Michaelis; Richard S. Zemel; Wieland Brendel; Matthias Bethge; Felix A. Wichmann (Nat. Mach. Intell., 2020)

Understanding and Mitigating Exploding Inverses in Invertible Neural Networks. - Jens Behrmann; Paul Vicol; Kuan-Chieh Wang; Roger B. Grosse; Jörn-Henrik Jacobsen (AISTATS, 2021)

Environment Inference for Invariant Learning. - Elliot Creager; Jörn-Henrik Jacobsen; Richard S. Zemel (ICML, 2021)

Out-of-Distribution Generalization via Risk Extrapolation (REx). - David Krueger 001; Ethan Caballero; Jörn-Henrik Jacobsen; Amy Zhang 001; Jonathan Binas; Dinghuai Zhang; Rémi Le Priol; Aaron C. Courville (ICML, 2021)

Learning Invariant Representations with Missing Data. - Mark Goldstein; Jörn-Henrik Jacobsen; Olina Chau; Adriel Saporta; Aahlad Manas Puli; Rajesh Ranganath; Andrew C. Miller (CLeaR, 2022)

Simulation-based Inference for Cardiovascular Models. - Antoine Wehenkel; Jens Behrmann; Andrew C. Miller; Guillermo Sapiro; Ozan Sener; Marco Cuturi; Jörn-Henrik Jacobsen (CoRR, 2023)

Robust Hybrid Learning With Expert Augmentation. - Antoine Wehenkel; Jens Behrmann; Hsiang Hsu; Guillermo Sapiro; Gilles Louppe; Jörn-Henrik Jacobsen (Trans. Mach. Learn. Res., 2023)

Addressing Misspecification in Simulation-based Inference through Data-driven Calibration. - Antoine Wehenkel; Juan L. Gamella; Ozan Sener; Jens Behrmann; Guillermo Sapiro; Marco Cuturi; Jörn-Henrik Jacobsen (CoRR, 2024)

Considerations for Distribution Shift Robustness of Diagnostic Models in Healthcare. - Arno Blaas; Adam Golinski; Andrew C. Miller; Luca Zappella; Jörn-Henrik Jacobsen; Christina Heinze-Deml (CoRR, 2024)

Leveraging Cardiovascular Simulations for In-Vivo Prediction of Cardiac Biomarkers. - Laura Manduchi; Antoine Wehenkel; Jens Behrmann; Luca Pegolotti; Andy C. Miller; Ozan Sener; Marco Cuturi; Guillermo Sapiro; Jörn-Henrik Jacobsen (CoRR, 2024)