Wieland Brendel

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 79

Top Topics

Causal Discovery

Learning Models

Supervised Learning

Top Venues

Publications

Prasanna Mayilvahanan, Thaddäus Wiedemer, Evgenia Rusak, Matthias Bethge, Wieland Brendel
Does CLIP's generalization performance mainly stem from high train-test similarity? ICLR (2024)
Goutham Rajendran, Patrik Reizinger, Wieland Brendel, Pradeep Kumar Ravikumar
An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis. CLeaR (2024)
Thaddäus Wiedemer, Jack Brady, Alexander Panfilov, Attila Juhos, Matthias Bethge, Wieland Brendel
Provable Compositional Generalization for Object-Centric Learning. ICLR (2024)
Patrik Reizinger, Szilvia Ujváry, Anna Mészáros, Anna Kerekes, Wieland Brendel, Ferenc Huszár
Understanding LLMs Requires More Than Statistical Generalization. CoRR (2024)
Evgenia Rusak, Patrik Reizinger, Attila Juhos, Oliver Bringmann, Roland S. Zimmermann, Wieland Brendel
InfoNCE: Identifying the Gap Between Theory and Practice. CoRR (2024)
Patrik Reizinger, Siyuan Guo, Ferenc Huszár, Bernhard Schölkopf, Wieland Brendel
Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning. CoRR (2024)
Amro Abbas, Evgenia Rusak, Kushal Tirumala, Wieland Brendel, Kamalika Chaudhuri, Ari S. Morcos
Effective pruning of web-scale datasets based on complexity of concept clusters. CoRR (2024)
Amro Abbas, Evgenia Rusak, Kushal Tirumala, Wieland Brendel, Kamalika Chaudhuri, Ari S. Morcos
Effective pruning of web-scale datasets based on complexity of concept clusters. ICLR (2024)
Zhe Li, Josue Ortega Caro, Evgenia Rusak, Wieland Brendel, Matthias Bethge, Fabio Anselmi, Ankit B. Patel, Andreas S. Tolias, Xaq Pitkow
Robust deep learning object recognition models rely on low frequency information in natural images. PLoS Comput. Biol. 19 (3) (2023)
Thaddäus Wiedemer, Prasanna Mayilvahanan, Matthias Bethge, Wieland Brendel
Compositional Generalization from First Principles. NeurIPS (2023)
Jack Brady, Roland S. Zimmermann, Yash Sharma, Bernhard Schölkopf, Julius von Kügelgen, Wieland Brendel
Provably Learning Object-Centric Representations. CoRR (2023)
Jack Brady, Roland S. Zimmermann, Yash Sharma, Bernhard Schölkopf, Julius von Kügelgen, Wieland Brendel
Provably Learning Object-Centric Representations. ICML (2023)
Prasanna Mayilvahanan, Thaddäus Wiedemer, Evgenia Rusak, Matthias Bethge, Wieland Brendel
Does CLIP's Generalization Performance Mainly Stem from High Train-Test Similarity? CoRR (2023)
Patrik Reizinger, Yash Sharma, Matthias Bethge, Bernhard Schölkopf, Ferenc Huszár, Wieland Brendel
Jacobian-based Causal Discovery with Nonlinear ICA. Trans. Mach. Learn. Res. 2023 (2023)
Robert Geirhos, Roland S. Zimmermann, Blair L. Bilodeau, Wieland Brendel, Been Kim
Don't trust your eyes: on the (un)reliability of feature visualizations. CoRR (2023)
Roland S. Zimmermann, Thomas Klein, Wieland Brendel
Scale Alone Does not Improve Mechanistic Interpretability in Vision Models. NeurIPS (2023)
Thaddäus Wiedemer, Jack Brady, Alexander Panfilov, Attila Juhos, Matthias Bethge, Wieland Brendel
Provable Compositional Generalization for Object-Centric Learning. CoRR (2023)
Dejana Mandic, Wieland Brendel, Claudio Michaelis
Iterative weakly supervised learning for novel class object detection. Tiny Papers @ ICLR (2023)
Thaddäus Wiedemer, Prasanna Mayilvahanan, Matthias Bethge, Wieland Brendel
Compositional Generalization from First Principles. CoRR (2023)
Roland S. Zimmermann, Thomas Klein, Wieland Brendel
Scale Alone Does not Improve Mechanistic Interpretability in Vision Models. CoRR (2023)
Goutham Rajendran, Patrik Reizinger, Wieland Brendel, Pradeep Ravikumar
An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis. CoRR (2023)
Roland S. Zimmermann, Wieland Brendel, Florian Tramèr, Nicholas Carlini
Increasing Confidence in Adversarial Robustness Evaluations. CoRR (2022)
Patrik Reizinger, Luigi Gresele, Jack Brady, Julius von Kügelgen, Dominik Zietlow, Bernhard Schölkopf, Georg Martius, Wieland Brendel, Michel Besserve
Embrace the Gap: VAEs Perform Independent Mechanism Analysis. CoRR (2022)
Lukas Schott, Julius von Kügelgen, Frederik Träuble, Peter Vincent Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, Wieland Brendel
Visual Representation Learning Does Not Generalize Strongly Within the Same Domain. ICLR (2022)
Evgenia Rusak, Steffen Schneider, George Pachitariu, Luisa Eck, Peter Vincent Gehler, Oliver Bringmann, Wieland Brendel, Matthias Bethge
If your data distribution shifts, use self-learning. Trans. Mach. Learn. Res. 2022 (2022)
Patrik Reizinger, Luigi Gresele, Jack Brady, Julius von Kügelgen, Dominik Zietlow, Bernhard Schölkopf, Georg Martius, Wieland Brendel, Michel Besserve
Embrace the Gap: VAEs Perform Independent Mechanism Analysis. NeurIPS (2022)
Roland S. Zimmermann, Wieland Brendel, Florian Tramèr, Nicholas Carlini
Increasing Confidence in Adversarial Robustness Evaluations. NeurIPS (2022)
Judy Borowski, Roland Simon Zimmermann, Judith Schepers, Robert Geirhos, Thomas S. A. Wallis, Matthias Bethge, Wieland Brendel
Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization. ICLR (2021)
Marissa A. Weis, Kashyap Chitta, Yash Sharma, Wieland Brendel, Matthias Bethge, Andreas Geiger, Alexander S. Ecker
Benchmarking Unsupervised Object Representations for Video Sequences. J. Mach. Learn. Res. 22 (2021)
Lukas Schott, Julius von Kügelgen, Frederik Träuble, Peter V. Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, Wieland Brendel
Visual Representation Learning Does Not Generalize Strongly Within the Same Domain. CoRR (2021)
Roland S. Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, Wieland Brendel
Contrastive Learning Inverts the Data Generating Process. ICML (2021)
Julius von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, Francesco Locatello
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style. CoRR (2021)
Evgenia Rusak, Steffen Schneider, Peter V. Gehler, Oliver Bringmann, Wieland Brendel, Matthias Bethge
Adapting ImageNet-scale models to complex distribution shifts with self-learning. CoRR (2021)
Roland S. Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, Wieland Brendel
Contrastive Learning Inverts the Data Generating Process. CoRR (2021)
David A. Klindt, Lukas Schott, Yash Sharma, Ivan Ustyuzhaninov, Wieland Brendel, Matthias Bethge, Dylan M. Paiton
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding. ICLR (2021)
Robert Geirhos, Kantharaju Narayanappa, Benjamin Mitzkus, Tizian Thieringer, Matthias Bethge, Felix A. Wichmann, Wieland Brendel
Partial success in closing the gap between human and machine vision. CoRR (2021)
Maura Pintor, Fabio Roli, Wieland Brendel, Battista Biggio
Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints. CoRR (2021)
Roland S. Zimmermann, Judy Borowski, Robert Geirhos, Matthias Bethge, Thomas S. A. Wallis, Wieland Brendel
How Well do Feature Visualizations Support Causal Understanding of CNN Activations? CoRR (2021)
Roland S. Zimmermann, Judy Borowski, Robert Geirhos, Matthias Bethge, Thomas S. A. Wallis, Wieland Brendel
How Well do Feature Visualizations Support Causal Understanding of CNN Activations? NeurIPS (2021)
Maura Pintor, Fabio Roli, Wieland Brendel, Battista Biggio
Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints. NeurIPS (2021)
Robert Geirhos, Kantharaju Narayanappa, Benjamin Mitzkus, Tizian Thieringer, Matthias Bethge, Felix A. Wichmann, Wieland Brendel
Partial success in closing the gap between human and machine vision. NeurIPS (2021)
Julius von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, Francesco Locatello
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style. NeurIPS (2021)
Marissa A. Weis, Kashyap Chitta, Yash Sharma, Wieland Brendel, Matthias Bethge, Andreas Geiger, Alexander S. Ecker
Unmasking the Inductive Biases of Unsupervised Object Representations for Video Sequences. CoRR (2020)
Steffen Schneider, Evgenia Rusak, Luisa Eck, Oliver Bringmann, Wieland Brendel, Matthias Bethge
Improving robustness against common corruptions by covariate shift adaptation. CoRR (2020)
Florian Tramèr, Nicholas Carlini, Wieland Brendel, Aleksander Madry
On Adaptive Attacks to Adversarial Example Defenses. NeurIPS (2020)
Steffen Schneider, Evgenia Rusak, Luisa Eck, Oliver Bringmann, Wieland Brendel, Matthias Bethge
Improving robustness against common corruptions by covariate shift adaptation. NeurIPS (2020)
Florian Tramèr, Nicholas Carlini, Wieland Brendel, Aleksander Madry
On Adaptive Attacks to Adversarial Example Defenses. CoRR (2020)
Robert Geirhos, Kantharaju Narayanappa, Benjamin Mitzkus, Matthias Bethge, Felix A. Wichmann, Wieland Brendel
On the surprising similarities between supervised and self-supervised models. CoRR (2020)
Jonas Rauber, Matthias Bethge, Wieland Brendel
EagerPy: Writing Code That Works Natively with PyTorch, TensorFlow, JAX, and NumPy. CoRR (2020)
Evgenia Rusak, Lukas Schott, Roland S. Zimmermann, Julian Bitterwolf, Oliver Bringmann, Matthias Bethge, Wieland Brendel
Increasing the robustness of DNNs against image corruptions by playing the Game of Noise. CoRR (2020)
Jonas Rauber, Roland Zimmermann, Matthias Bethge, Wieland Brendel
Foolbox Native: Fast adversarial attacks to benchmark the robustness of machine learning models in PyTorch, TensorFlow, and JAX. J. Open Source Softw. 5 (53) (2020)
David Klindt, Lukas Schott, Yash Sharma, Ivan Ustyuzhaninov, Wieland Brendel, Matthias Bethge, Dylan M. Paiton
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding. CoRR (2020)
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard S. Zemel, Wieland Brendel, Matthias Bethge, Felix A. Wichmann
Shortcut learning in deep neural networks. Nat. Mach. Intell. 2 (11) (2020)
Evgenia Rusak, Lukas Schott, Roland S. Zimmermann, Julian Bitterwolf, Oliver Bringmann, Matthias Bethge, Wieland Brendel
A Simple Way to Make Neural Networks Robust Against Diverse Image Corruptions. ECCV (3) (2020)
Wieland Brendel, Ralph Bourdoukan, Pietro Vertechi, Christian K. Machens, Sophie Denève
Learning to represent signals spike by spike. PLoS Comput. Biol. 16 (3) (2020)
Judy Borowski, Roland S. Zimmermann, Judith Schepers, Robert Geirhos, Thomas S. A. Wallis, Matthias Bethge, Wieland Brendel
Exemplary Natural Images Explain CNN Activations Better than Feature Visualizations. CoRR (2020)
Christina M. Funke, Judy Borowski, Karolina Stosio, Wieland Brendel, Thomas S. A. Wallis, Matthias Bethge
The Notorious Difficulty of Comparing Human and Machine Perception. CoRR (2020)
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard S. Zemel, Wieland Brendel, Matthias Bethge, Felix A. Wichmann
Shortcut Learning in Deep Neural Networks. CoRR (2020)
Lukas Schott, Jonas Rauber, Matthias Bethge, Wieland Brendel
Towards the first adversarially robust neural network model on MNIST. ICLR (Poster) (2019)
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, Wieland Brendel
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. ICLR (2019)
Claudio Michaelis, Benjamin Mitzkus, Robert Geirhos, Evgenia Rusak, Oliver Bringmann, Alexander S. Ecker, Matthias Bethge, Wieland Brendel
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming. CoRR (2019)
Zhe Li, Wieland Brendel, Edgar Y. Walker, Erick Cobos, Taliah Muhammad, Jacob Reimer, Matthias Bethge, Fabian H. Sinz, Xaq Pitkow, Andreas S. Tolias
Learning From Brains How to Regularize Machines. CoRR (2019)
Zhe Li, Wieland Brendel, Edgar Y. Walker, Erick Cobos, Taliah Muhammad, Jacob Reimer, Matthias Bethge, Fabian H. Sinz, Zachary Pitkow, Andreas S. Tolias
Learning from brains how to regularize machines. NeurIPS (2019)
Wieland Brendel, Jonas Rauber, Matthias Kümmerer, Ivan Ustyuzhaninov, Matthias Bethge
Accurate, reliable and fast robustness evaluation. CoRR (2019)
Wieland Brendel, Matthias Bethge
Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet. CoRR (2019)
Wieland Brendel, Jonas Rauber, Matthias Kümmerer, Ivan Ustyuzhaninov, Matthias Bethge
Accurate, reliable and fast robustness evaluation. NeurIPS (2019)
Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian J. Goodfellow, Aleksander Madry, Alexey Kurakin
On Evaluating Adversarial Robustness. CoRR (2019)
Wieland Brendel, Matthias Bethge
Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet. ICLR (Poster) (2019)
Alexander Böttcher, Wieland Brendel, Bernhard Englitz, Matthias Bethge
Trace your sources in large-scale data: one ring to find them all. CoRR (2018)
Lukas Schott, Jonas Rauber, Wieland Brendel, Matthias Bethge
Robust Perception through Analysis by Synthesis. CoRR (2018)
Ivan Ustyuzhaninov, Claudio Michaelis, Wieland Brendel, Matthias Bethge
One-shot Texture Segmentation. CoRR (2018)
Wieland Brendel, Jonas Rauber, Matthias Bethge
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models. ICLR (Poster) (2018)
Wieland Brendel, Jonas Rauber, Alexey Kurakin, Nicolas Papernot, Behar Veliqi, Marcel Salathé, Sharada P. Mohanty, Matthias Bethge
Adversarial Vision Challenge. CoRR (2018)
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, Wieland Brendel
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. CoRR (2018)
Ivan Ustyuzhaninov, Wieland Brendel, Leon A. Gatys, Matthias Bethge
What does it take to generate natural textures? ICLR (Poster) (2017)
Wieland Brendel, Jonas Rauber, Matthias Bethge
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models. CoRR (2017)
Wieland Brendel, Matthias Bethge
Comment on "Biologically inspired protection of deep networks from adversarial attacks". CoRR (2017)
Jonas Rauber, Wieland Brendel, Matthias Bethge
Foolbox v0.8.0: A Python toolbox to benchmark the robustness of machine learning models. CoRR (2017)
Ivan Ustyuzhaninov, Wieland Brendel, Leon A. Gatys, Matthias Bethge
Texture Synthesis Using Shallow Convolutional Networks with Random Filters. CoRR (2016)
Pietro Vertechi, Wieland Brendel, Christian K. Machens
Unsupervised learning of an efficient short-term memory network. NIPS (2014)
Wieland Brendel, Ranulfo Romo, Christian K. Machens
Demixed Principal Component Analysis. NIPS (2011)