Brady*, J., Zimmermann*, R. S., Sharma, Y., Schölkopf, B., von Kügelen, J., Brendel, W. Provably Learning Object-Centric Representations Proceedings of the 40th International Conference on Machine Learning (ICML), 202, pages: 3038-3062, Proceedings of Machine Learning Research, (Editors: A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato and J. Scarlett), PMLR, July 2023, *equal contribution (conference)

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Reizinger*, P., Gresele*, L., Brady*, J., von Kügelgen, J., Zietlow, D., Schölkopf, B., Martius, G., Brendel, W., Besserve, M. Embrace the Gap: VAEs Perform Independent Mechanism Analysis Advances in Neural Information Processing Systems (NeurIPS 2022), 35, pages: 12040-12057, (Editors: S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh), Curran Associates, Inc., 36th Annual Conference on Neural Information Processing Systems, December 2022, *equal first authorship (conference)

Arxiv PDF link (url) [BibTex]

Rusak, Evgenia, Schneider, Steffen, Gehler, Peter Vincent, Bringmann, Oliver, Brendel, Wieland, Bethge, Matthias ImageNet-D: A new challenging robustness dataset inspired by domain adaptation In ICML 2022 Shift Happens Workshop, 2022 (inproceedings)

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Schott, Lukas, von Kügelgen, Julius, Träuble, Frederik, Gehler, Peter, Russell, Chris, Bethge, Matthias, Schölkopf, Bernhard, Locatello, Francesco, Brendel, Wieland Visual representation learning does not generalize strongly within the same domain In Tenth International Conference on Learning Representations (ICLR 2022), 2021 (inproceedings)

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Zimmermann, Roland S, Sharma, Yash, Schneider, Steffen, Bethge, Matthias, Brendel, Wieland Contrastive Learning Inverts the Data Generating Process In International Conference on Machine Learning (ICML 2021), 2021 (inproceedings)

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von Kügelgen, Julius, Sharma, Yash, Gresele, Luigi, Brendel, Wieland, Schölkopf, Bernhard, Besserve, Michel, Locatello, Francesco Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style In 35th Conference on Neural Information Processing Systems (NeurIPS), 2021 (inproceedings)

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Pintor, Maura, Roli, Fabio, Brendel, Wieland, Biggio, Battista Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints In 35th Conference on Neural Information Processing Systems (NeurIPS), 2021 (inproceedings)

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Geirhos, Robert, Narayanappa, Kantharaju, Mitzkus, Benjamin, Thieringer, Tizian, Bethge, Matthias, Wichmann, Felix A, Brendel, Wieland Partial success in closing the gap between human and machine vision In 35th Conference on Neural Information Processing Systems (NeurIPS), 2021 (inproceedings)

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Zimmermann, Roland S, Borowski, Judy, Geirhos, Robert, Bethge, Matthias, Wallis, Thomas SA, Brendel, Wieland How Well do Feature Visualizations Support Causal Understanding of CNN Activations? In 35th Conference on Neural Information Processing Systems (NeurIPS), 2021 (inproceedings)

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Klindt, David, Schott, Lukas, Sharma, Yash, Ustyuzhaninov, Ivan, Brendel, Wieland, Bethge, Matthias, Paiton, Dylan Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding In International Conference on Learning Representations (ICLR), 2020 (inproceedings)

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Borowski, Judy, Zimmermann, Roland S, Schepers, Judith, Geirhos, Robert, Wallis, Thomas SA, Bethge, Matthias, Brendel, Wieland Exemplary Natural Images Explain CNN Activations Better than Feature Visualizations In International Conference on Learning Representations (ICLR 2021), 2020 (inproceedings)

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Tramer, Florian, Carlini, Nicholas, Brendel, Wieland, Madry, Aleksander On adaptive attacks to adversarial example defenses In 34th Conference on Neural Information Processing Systems (NeurIPS), 2020 (inproceedings)

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Rusak, Evgenia, Schott, Lukas, Zimmermann, Roland, Bitterwolf, Julian, Bringmann, Oliver, Bethge, Matthias, Brendel, Wieland Increasing the robustness of DNNs against image corruptions by playing the Game of Noise In European Conference on Computer Vision (oral), 2020 (inproceedings)

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Rusak, Evgenia, Schott, Lukas, Zimmermann, Roland S, Bitterwolf, Julian, Bringmann, Oliver, Bethge, Matthias, Brendel, Wieland A simple way to make neural networks robust against diverse image corruptions In European Conference on Computer Vision, pages: 53-69, 2020 (inproceedings)

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Schneider, Steffen, Rusak, Evgenia, Eck, Luisa, Bringmann, Oliver, Brendel, Wieland, Bethge, Matthias Improving robustness against common corruptions by covariate shift adaptation In 34th Conference on Neural Information Processing Systems (NeurIPS), 2020 (inproceedings)

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Geirhos, Robert, Narayanappa, Kantharaju, Mitzkus, Benjamin, Bethge, Matthias, Wichmann, Felix A, Brendel, Wieland On the surprising similarities between supervised and self-supervised models In Shared Visual Representations in Humans & Machines Workshop (NeurIPS 2020), 2020 (inproceedings)

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Geirhos, Robert, Jacobsen, Jörn-Henrik, Michaelis, Claudio, Zemel, Richard, Brendel, Wieland, Bethge, Matthias, Wichmann, Felix A Unintended cue learning: Lessons for deep learning from experimental psychology In 20th Annual Meeting of the Vision Sciences Society (VSS 2020), 652-652, 2020 (inproceedings)

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Michaelis, Claudio, Mitzkus, Benjamin, Geirhos, Robert, Rusak, Evgenia, Bringmann, Oliver, Ecker, Alexander S, Bethge, Matthias, Brendel, Wieland Benchmarking robustness in object detection: Autonomous driving when winter is coming In NeurIPS 2019 Workshop on Machine Learning for Autonomous Driving, 2019 (inproceedings)

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Li, Zhe, Brendel, Wieland, Walker, Edgar, Cobos, Erick, Muhammad, Taliah, Reimer, Jacob, Bethge, Matthias, Sinz, Fabian, Pitkow, Zachary, Tolias, Andreas Learning from brains how to regularize machines In 33rd Conference on Neural Information Processing Systems (NeurIPS), pages: 9525-9535, 2019 (inproceedings)

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Brendel, Wieland, Bethge, Matthias Approximating cnns with bag-of-local-features models works surprisingly well on imagenet In Seventh International Conference on Learning Representations (ICLR 2019), 2019 (inproceedings)

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Brendel, Wieland, Rauber, Jonas, Kümmerer, Matthias, Ustyuzhaninov, Ivan, Bethge, Matthias Accurate, reliable and fast robustness evaluation In 33rd Conference on Neural Information Processing Systems (NeurIPS), pages: 12841-12851, 2019 (inproceedings)

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Geirhos, Robert, Rubisch, Patricia, Michaelis, Claudio, Bethge, Matthias, Wichmann, Felix A, Brendel, Wieland ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness In Seventh International Conference on Learning Representations (ICLR 2019), 2018 (inproceedings)

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Schott, Lukas, Rauber, Jonas, Bethge, Matthias, Brendel, Wieland Towards the first adversarially robust neural network model on MNIST In Seventh International Conference on Learning Representations (ICLR 2019), 2018 (inproceedings)

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Brendel, Wieland, Rauber, Jonas, Bethge, Matthias Decision-based adversarial attacks: Reliable attacks against black-box machine learning models In Sixth International Conference on Learning Representations (ICLR 2018), 2017 (inproceedings)

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Ustyuzhaninov, Ivan, Brendel, Wieland, Gatys, Leon A, Bethge, Matthias What does it take to generate natural textures? In International Conference on Learning Representations (ICLR), 2017 (inproceedings)

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Rauber, Jonas, Brendel, Wieland, Bethge, Matthias Foolbox v0. 8.0: A python toolbox to benchmark the robustness of machine learning models In Reliable Machine Learning in the Wild Workshop, 34th International Conference on Machine Learning (ICML), 5, 2017 (inproceedings)

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Boettcher, A, Brendel, W, Bethge, M Large scale blind source separation In Bernstein Conference 2016, pages: 118-119, 2016 (inproceedings)

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Vertechi, Pietro, Brendel, Wieland, Machens, Christian K Unsupervised learning of an efficient short-term memory network In 28th Conference on Neural Information Processing Systems (NeurIPS), pages: 3653-3661, 2014 (inproceedings)

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Brendel, Wieland, Romo, Ranulfo, Machens, Christian K Demixed principal component analysis In Advances in Neural Information Processing Systems 24 (NIPS 2011), pages: 2654-2662, 2011 (inproceedings)

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Reizinger, Patrik, Sharma, Yash, Bethge, Matthias, Schölkopf, Bernhard, Huszár, Ferenc, Brendel, Wieland Multivariable Causal Discovery with General Nonlinear Relationships In UAI 2022 Workshop on Causal Representation Learning (inproceedings)

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