Wieland Brendel

113 papers A* 31A 1Journal 70Unranked 11

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Causality is Key for Interpretability Claims to Generalise. CoRR Shruti Joshi, Aaron Mueller, David A. Klindt, Wieland Brendel, Patrik Reizinger, Dhanya Sridhar
2026	J	`jnl`	Low-Pass Filtering Improves Behavioral Alignment of Vision Models. CoRR Max Wolff, Thomas Klein, Evgenia Rusak, Felix A. Wichmann, Wieland Brendel
2026	J	`jnl`	MentisOculi: Revealing the Limits of Reasoning with Mental Imagery. CoRR Jana Zeller, Thaddäus Wiedemer, Fanfei Li, Thomas Klein, Prasanna Mayilvahanan, Matthias Bethge, Felix A. Wichmann, Ryan Cotterell, Wieland Brendel
2026	J	`jnl`	Who Guards the Guardians? The Challenges of Evaluating Identifiability of Learned Representations. CoRR Shruti Joshi, Théo Saulus, Wieland Brendel, Philippe Brouillard, Dhanya Sridhar, Patrik Reizinger
2025	J	`jnl`	An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research. CoRR Patrik Reizinger, Randall Balestriero, David A. Klindt, Wieland Brendel
2025	A*	`conf`	Cross-Entropy Is All You Need To Invert the Data Generating Process. ICLR Patrik Reizinger, Alice Bizeul, Attila Juhos, Julia E. Vogt, Randall Balestriero, Wieland Brendel, David A. Klindt
2025	J	`jnl`	Estimating Treatment Effects with Independent Component Analysis. CoRR Patrik Reizinger, Lester Mackey, Wieland Brendel, Rahul Krishnan
2025	J	`jnl`	Generation is Required for Data-Efficient Perception. CoRR Jack Brady, Bernhard Schölkopf, Thomas Kipf, Simon Buchholz, Wieland Brendel
2025	A*	`conf`	Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning. ICLR Patrik Reizinger, Siyuan Guo, Ferenc Huszár, Bernhard Schölkopf, Wieland Brendel
2025	A*	`conf`	In Search of Forgotten Domain Generalization. ICLR Prasanna Mayilvahanan, Roland S. Zimmermann, Thaddäus Wiedemer, Evgenia Rusak, Attila Juhos, Matthias Bethge, Wieland Brendel
2025	A*	`conf`	In Search of Forgotten Domain Generalization. ICLR Prasanna Mayilvahanan, Roland S. Zimmermann, Thaddäus Wiedemer, Evgenia Rusak, Attila Juhos, Matthias Bethge, Wieland Brendel
2025	A	`conf`	InfoNCE: Identifying the Gap Between Theory and Practice. AISTATS Evgenia Rusak, Patrik Reizinger, Attila Juhos, Oliver Bringmann, Roland S. Zimmermann, Wieland Brendel
2025	A*	`conf`	Interaction Asymmetry: A General Principle for Learning Composable Abstractions. ICLR Jack Brady, Julius von Kügelgen, Sébastien Lachapelle, Simon Buchholz, Thomas Kipf, Wieland Brendel
2025	A*	`conf`	LAION-C: An Out-of-Distribution Benchmark for Web-Scale Vision Models. ICML Fanfei Li, Thomas Klein, Wieland Brendel, Robert Geirhos, Roland S. Zimmermann
2025	J	`jnl`	LAION-C: An Out-of-Distribution Benchmark for Web-Scale Vision Models. CoRR Fanfei Li, Thomas Klein, Wieland Brendel, Robert Geirhos, Roland S. Zimmermann
2025	A*	`conf`	LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws. ICML Prasanna Mayilvahanan, Thaddäus Wiedemer, Sayak Mallick, Matthias Bethge, Wieland Brendel
2025	J	`jnl`	LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws. CoRR Prasanna Mayilvahanan, Thaddäus Wiedemer, Sayak Mallick, Matthias Bethge, Wieland Brendel
2025	J	`jnl`	MATH-Beyond: A Benchmark for RL to Expand Beyond the Base Model. CoRR Prasanna Mayilvahanan, Ricardo Dominguez-Olmedo, Thaddäus Wiedemer, Wieland Brendel
2025	J	`jnl`	Occam's Razor for SSL: Memory-Efficient Parametric Instance Discrimination. Trans. Mach. Learn. Res. Eric Gan, Patrik Reizinger, Alice Bizeul, Attila Juhos, Mark Ibrahim, Randall Balestriero, David A. Klindt, Wieland Brendel, Baharan Mirzasoleiman
2025	—	`conf`	Position: An Empirically Grounded Identifiability Theory Will Accelerate Self Supervised Learning Research. ICML (Position Papers) Patrik Reizinger, Randall Balestriero, David A. Klindt, Wieland Brendel
2025	J	`jnl`	Pretraining Frequency Predicts Compositional Generalization of CLIP on Real-World Tasks. CoRR Thaddäus Wiedemer, Yash Sharma, Ameya Prabhu, Matthias Bethge, Wieland Brendel
2025	J	`jnl`	Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning. CoRR Patrik Reizinger, Bálint Mucsányi, Siyuan Guo, Benjamin Eysenbach, Bernhard Schölkopf, Wieland Brendel
2025	J	`jnl`	VGGSounder: Audio-Visual Evaluations for Foundation Models. CoRR Daniil Zverev, Thaddäus Wiedemer, Ameya Prabhu, Matthias Bethge, Wieland Brendel, A. Sophia Koepke
2024	—	`conf`	An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis. CLeaR Goutham Rajendran, Patrik Reizinger, Wieland Brendel, Pradeep Kumar Ravikumar
2024	J	`jnl`	Cross-Entropy Is All You Need To Invert the Data Generating Process. CoRR Patrik Reizinger, Alice Bizeul, Attila Juhos, Julia E. Vogt, Randall Balestriero, Wieland Brendel, David A. Klindt
2024	A*	`conf`	Does CLIP's generalization performance mainly stem from high train-test similarity? ICLR Prasanna Mayilvahanan, Thaddäus Wiedemer, Evgenia Rusak, Matthias Bethge, Wieland Brendel
2024	A*	`conf`	Don't trust your eyes: on the (un)reliability of feature visualizations. ICML Robert Geirhos, Roland S. Zimmermann, Blair L. Bilodeau, Wieland Brendel, Been Kim
2024	A*	`conf`	Effective pruning of web-scale datasets based on complexity of concept clusters. ICLR Amro Abbas, Evgenia Rusak, Kushal Tirumala, Wieland Brendel, Kamalika Chaudhuri, Ari S. Morcos
2024	J	`jnl`	Effective pruning of web-scale datasets based on complexity of concept clusters. CoRR Amro Abbas, Evgenia Rusak, Kushal Tirumala, Wieland Brendel, Kamalika Chaudhuri, Ari S. Morcos
2024	J	`jnl`	Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning. CoRR Patrik Reizinger, Siyuan Guo, Ferenc Huszár, Bernhard Schölkopf, Wieland Brendel
2024	J	`jnl`	In Search of Forgotten Domain Generalization. CoRR Prasanna Mayilvahanan, Roland S. Zimmermann, Thaddäus Wiedemer, Evgenia Rusak, Attila Juhos, Matthias Bethge, Wieland Brendel
2024	J	`jnl`	InfoNCE: Identifying the Gap Between Theory and Practice. CoRR Evgenia Rusak, Patrik Reizinger, Attila Juhos, Oliver Bringmann, Roland S. Zimmermann, Wieland Brendel
2024	J	`jnl`	Interaction Asymmetry: A General Principle for Learning Composable Abstractions. CoRR Jack Brady, Julius von Kügelgen, Sébastien Lachapelle, Simon Buchholz, Thomas Kipf, Wieland Brendel
2024	A*	`conf`	Measuring Per-Unit Interpretability at Scale Without Humans. NeurIPS Roland S. Zimmermann, David A. Klindt, Wieland Brendel
2024	A*	`conf`	Position: Understanding LLMs Requires More Than Statistical Generalization. ICML Patrik Reizinger, Szilvia Ujváry, Anna Mészáros, Anna Kerekes, Wieland Brendel, Ferenc Huszár
2024	A*	`conf`	Provable Compositional Generalization for Object-Centric Learning. ICLR Thaddäus Wiedemer, Jack Brady, Alexander Panfilov, Attila Juhos, Matthias Bethge, Wieland Brendel
2024	A*	`conf`	Rule Extrapolation in Language Modeling: A Study of Compositional Generalization on OOD Prompts. NeurIPS Anna Mészáros, Szilvia Ujváry, Wieland Brendel, Patrik Reizinger, Ferenc Huszar
2024	J	`jnl`	Rule Extrapolation in Language Models: A Study of Compositional Generalization on OOD Prompts. CoRR Anna Mészáros, Szilvia Ujváry, Wieland Brendel, Patrik Reizinger, Ferenc Huszár
2024	J	`jnl`	Translational symmetry in convolutions with localized kernels causes an implicit bias toward high frequency adversarial examples. Frontiers Comput. Neurosci. Josue Ortega Caro, Yilong Ju, Ryan Pyle, Sourav Dey, Wieland Brendel, Fabio Anselmi, Ankit B. Patel
2024	J	`jnl`	Understanding LLMs Requires More Than Statistical Generalization. CoRR Patrik Reizinger, Szilvia Ujváry, Anna Mészáros, Anna Kerekes, Wieland Brendel, Ferenc Huszár
2023	J	`jnl`	An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis. CoRR Goutham Rajendran, Patrik Reizinger, Wieland Brendel, Pradeep Ravikumar
2023	A*	`conf`	Compositional Generalization from First Principles. NeurIPS Thaddäus Wiedemer, Prasanna Mayilvahanan, Matthias Bethge, Wieland Brendel
2023	J	`jnl`	Compositional Generalization from First Principles. CoRR Thaddäus Wiedemer, Prasanna Mayilvahanan, Matthias Bethge, Wieland Brendel
2023	J	`jnl`	Does CLIP's Generalization Performance Mainly Stem from High Train-Test Similarity? CoRR Prasanna Mayilvahanan, Thaddäus Wiedemer, Evgenia Rusak, Matthias Bethge, Wieland Brendel
2023	J	`jnl`	Don't trust your eyes: on the (un)reliability of feature visualizations. CoRR Robert Geirhos, Roland S. Zimmermann, Blair L. Bilodeau, Wieland Brendel, Been Kim
2023	—	`conf`	Iterative weakly supervised learning for novel class object detection. Tiny Papers @ ICLR Dejana Mandic, Wieland Brendel, Claudio Michaelis
2023	J	`jnl`	Jacobian-based Causal Discovery with Nonlinear ICA. Trans. Mach. Learn. Res. Patrik Reizinger, Yash Sharma, Matthias Bethge, Bernhard Schölkopf, Ferenc Huszár, Wieland Brendel
2023	J	`jnl`	Provable Compositional Generalization for Object-Centric Learning. CoRR Thaddäus Wiedemer, Jack Brady, Alexander Panfilov, Attila Juhos, Matthias Bethge, Wieland Brendel
2023	A*	`conf`	Provably Learning Object-Centric Representations. ICML Jack Brady, Roland S. Zimmermann, Yash Sharma, Bernhard Schölkopf, Julius von Kügelgen, Wieland Brendel
2023	J	`jnl`	Provably Learning Object-Centric Representations. CoRR Jack Brady, Roland S. Zimmermann, Yash Sharma, Bernhard Schölkopf, Julius von Kügelgen, Wieland Brendel
2023	J	`jnl`	Robust deep learning object recognition models rely on low frequency information in natural images. PLoS Comput. Biol. Zhe Li, Josue Ortega Caro, Evgenia Rusak, Wieland Brendel, Matthias Bethge, Fabio Anselmi, Ankit B. Patel, Andreas S. Tolias, Xaq Pitkow
2023	A*	`conf`	Scale Alone Does not Improve Mechanistic Interpretability in Vision Models. NeurIPS Roland S. Zimmermann, Thomas Klein, Wieland Brendel
2023	J	`jnl`	Scale Alone Does not Improve Mechanistic Interpretability in Vision Models. CoRR Roland S. Zimmermann, Thomas Klein, Wieland Brendel
2022	A*	`conf`	Embrace the Gap: VAEs Perform Independent Mechanism Analysis. NeurIPS Patrik Reizinger, Luigi Gresele, Jack Brady, Julius von Kügelgen, Dominik Zietlow, Bernhard Schölkopf, Georg Martius, Wieland Brendel, Michel Besserve
2022	J	`jnl`	Embrace the Gap: VAEs Perform Independent Mechanism Analysis. CoRR Patrik Reizinger, Luigi Gresele, Jack Brady, Julius von Kügelgen, Dominik Zietlow, Bernhard Schölkopf, Georg Martius, Wieland Brendel, Michel Besserve
2022	J	`jnl`	If your data distribution shifts, use self-learning. Trans. Mach. Learn. Res. Evgenia Rusak, Steffen Schneider, George Pachitariu, Luisa Eck, Peter Vincent Gehler, Oliver Bringmann, Wieland Brendel, Matthias Bethge
2022	A*	`conf`	Increasing Confidence in Adversarial Robustness Evaluations. NeurIPS Roland S. Zimmermann, Wieland Brendel, Florian Tramèr, Nicholas Carlini
2022	J	`jnl`	Increasing Confidence in Adversarial Robustness Evaluations. CoRR Roland S. Zimmermann, Wieland Brendel, Florian Tramèr, Nicholas Carlini
2022	A*	`conf`	Visual Representation Learning Does Not Generalize Strongly Within the Same Domain. ICLR Lukas Schott, Julius von Kügelgen, Frederik Träuble, Peter Vincent Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, Wieland Brendel
2021	J	`jnl`	Adapting ImageNet-scale models to complex distribution shifts with self-learning. CoRR Evgenia Rusak, Steffen Schneider, Peter V. Gehler, Oliver Bringmann, Wieland Brendel, Matthias Bethge
2021	J	`jnl`	Benchmarking Unsupervised Object Representations for Video Sequences. J. Mach. Learn. Res. Marissa A. Weis, Kashyap Chitta, Yash Sharma, Wieland Brendel, Matthias Bethge, Andreas Geiger, Alexander S. Ecker
2021	A*	`conf`	Contrastive Learning Inverts the Data Generating Process. ICML Roland S. Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, Wieland Brendel
2021	J	`jnl`	Contrastive Learning Inverts the Data Generating Process. CoRR Roland S. Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, Wieland Brendel
2021	A*	`conf`	Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization. ICLR Judy Borowski, Roland Simon Zimmermann, Judith Schepers, Robert Geirhos, Thomas S. A. Wallis, Matthias Bethge, Wieland Brendel
2021	A*	`conf`	Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints. NeurIPS Maura Pintor, Fabio Roli, Wieland Brendel, Battista Biggio
2021	J	`jnl`	Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints. CoRR Maura Pintor, Fabio Roli, Wieland Brendel, Battista Biggio
2021	A*	`conf`	How Well do Feature Visualizations Support Causal Understanding of CNN Activations? NeurIPS Roland S. Zimmermann, Judy Borowski, Robert Geirhos, Matthias Bethge, Thomas S. A. Wallis, Wieland Brendel
2021	J	`jnl`	How Well do Feature Visualizations Support Causal Understanding of CNN Activations? CoRR Roland S. Zimmermann, Judy Borowski, Robert Geirhos, Matthias Bethge, Thomas S. A. Wallis, Wieland Brendel
2021	A*	`conf`	Partial success in closing the gap between human and machine vision. NeurIPS Robert Geirhos, Kantharaju Narayanappa, Benjamin Mitzkus, Tizian Thieringer, Matthias Bethge, Felix A. Wichmann, Wieland Brendel
2021	J	`jnl`	Partial success in closing the gap between human and machine vision. CoRR Robert Geirhos, Kantharaju Narayanappa, Benjamin Mitzkus, Tizian Thieringer, Matthias Bethge, Felix A. Wichmann, Wieland Brendel
2021	A*	`conf`	Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style. NeurIPS Julius von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, Francesco Locatello
2021	J	`jnl`	Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style. CoRR Julius von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, Francesco Locatello
2021	A*	`conf`	Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding. ICLR David A. Klindt, Lukas Schott, Yash Sharma, Ivan Ustyuzhaninov, Wieland Brendel, Matthias Bethge, Dylan M. Paiton
2021	J	`jnl`	Visual Representation Learning Does Not Generalize Strongly Within the Same Domain. CoRR Lukas Schott, Julius von Kügelgen, Frederik Träuble, Peter V. Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, Wieland Brendel
2020	—	`conf`	A Simple Way to Make Neural Networks Robust Against Diverse Image Corruptions. ECCV (3) Evgenia Rusak, Lukas Schott, Roland S. Zimmermann, Julian Bitterwolf, Oliver Bringmann, Matthias Bethge, Wieland Brendel
2020	J	`jnl`	EagerPy: Writing Code That Works Natively with PyTorch, TensorFlow, JAX, and NumPy. CoRR Jonas Rauber, Matthias Bethge, Wieland Brendel
2020	J	`jnl`	Exemplary Natural Images Explain CNN Activations Better than Feature Visualizations. CoRR Judy Borowski, Roland S. Zimmermann, Judith Schepers, Robert Geirhos, Thomas S. A. Wallis, Matthias Bethge, Wieland Brendel
2020	J	`jnl`	Foolbox Native: Fast adversarial attacks to benchmark the robustness of machine learning models in PyTorch, TensorFlow, and JAX. J. Open Source Softw. Jonas Rauber, Roland Zimmermann, Matthias Bethge, Wieland Brendel
2020	A*	`conf`	Improving robustness against common corruptions by covariate shift adaptation. NeurIPS Steffen Schneider, Evgenia Rusak, Luisa Eck, Oliver Bringmann, Wieland Brendel, Matthias Bethge
2020	J	`jnl`	Improving robustness against common corruptions by covariate shift adaptation. CoRR Steffen Schneider, Evgenia Rusak, Luisa Eck, Oliver Bringmann, Wieland Brendel, Matthias Bethge
2020	J	`jnl`	Increasing the robustness of DNNs against image corruptions by playing the Game of Noise. CoRR Evgenia Rusak, Lukas Schott, Roland S. Zimmermann, Julian Bitterwolf, Oliver Bringmann, Matthias Bethge, Wieland Brendel
2020	J	`jnl`	Learning to represent signals spike by spike. PLoS Comput. Biol. Wieland Brendel, Ralph Bourdoukan, Pietro Vertechi, Christian K. Machens, Sophie Denève
2020	A*	`conf`	On Adaptive Attacks to Adversarial Example Defenses. NeurIPS Florian Tramèr, Nicholas Carlini, Wieland Brendel, Aleksander Madry
2020	J	`jnl`	On Adaptive Attacks to Adversarial Example Defenses. CoRR Florian Tramèr, Nicholas Carlini, Wieland Brendel, Aleksander Madry
2020	J	`jnl`	On the surprising similarities between supervised and self-supervised models. CoRR Robert Geirhos, Kantharaju Narayanappa, Benjamin Mitzkus, Matthias Bethge, Felix A. Wichmann, Wieland Brendel
2020	J	`jnl`	Shortcut Learning in Deep Neural Networks. CoRR Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard S. Zemel, Wieland Brendel, Matthias Bethge, Felix A. Wichmann
2020	J	`jnl`	Shortcut learning in deep neural networks. Nat. Mach. Intell. Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard S. Zemel, Wieland Brendel, Matthias Bethge, Felix A. Wichmann
2020	J	`jnl`	The Notorious Difficulty of Comparing Human and Machine Perception. CoRR Christina M. Funke, Judy Borowski, Karolina Stosio, Wieland Brendel, Thomas S. A. Wallis, Matthias Bethge
2020	J	`jnl`	Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding. CoRR David A. Klindt, Lukas Schott, Yash Sharma, Ivan Ustyuzhaninov, Wieland Brendel, Matthias Bethge, Dylan M. Paiton
2020	J	`jnl`	Unmasking the Inductive Biases of Unsupervised Object Representations for Video Sequences. CoRR Marissa A. Weis, Kashyap Chitta, Yash Sharma, Wieland Brendel, Matthias Bethge, Andreas Geiger, Alexander S. Ecker
2019	A*	`conf`	Accurate, reliable and fast robustness evaluation. NeurIPS Wieland Brendel, Jonas Rauber, Matthias Kümmerer, Ivan Ustyuzhaninov, Matthias Bethge
2019	J	`jnl`	Accurate, reliable and fast robustness evaluation. CoRR Wieland Brendel, Jonas Rauber, Matthias Kümmerer, Ivan Ustyuzhaninov, Matthias Bethge
2019	—	`conf`	Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet. ICLR (Poster) Wieland Brendel, Matthias Bethge
2019	J	`jnl`	Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet. CoRR Wieland Brendel, Matthias Bethge
2019	J	`jnl`	Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming. CoRR Claudio Michaelis, Benjamin Mitzkus, Robert Geirhos, Evgenia Rusak, Oliver Bringmann, Alexander S. Ecker, Matthias Bethge, Wieland Brendel
2019	—	`conf`	ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. ICLR Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, Wieland Brendel
2019	J	`jnl`	Learning From Brains How to Regularize Machines. CoRR Zhe Li, Wieland Brendel, Edgar Y. Walker, Erick Cobos, Taliah Muhammad, Jacob Reimer, Matthias Bethge, Fabian H. Sinz, Xaq Pitkow, Andreas S. Tolias
2019	A*	`conf`	Learning from brains how to regularize machines. NeurIPS Zhe Li, Wieland Brendel, Edgar Y. Walker, Erick Cobos, Taliah Muhammad, Jacob Reimer, Matthias Bethge, Fabian H. Sinz, Xaq Pitkow, Andreas S. Tolias
2019	J	`jnl`	On Evaluating Adversarial Robustness. CoRR Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian J. Goodfellow, Aleksander Madry, Alexey Kurakin
2019	—	`conf`	Towards the first adversarially robust neural network model on MNIST. ICLR (Poster) Lukas Schott, Jonas Rauber, Matthias Bethge, Wieland Brendel
2018	J	`jnl`	Adversarial Vision Challenge. CoRR Wieland Brendel, Jonas Rauber, Alexey Kurakin, Nicolas Papernot, Behar Veliqi, Marcel Salathé, Sharada P. Mohanty, Matthias Bethge
2018	—	`conf`	Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models. ICLR (Poster) Wieland Brendel, Jonas Rauber, Matthias Bethge
2018	J	`jnl`	ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. CoRR Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, Wieland Brendel
2018	J	`jnl`	One-shot Texture Segmentation. CoRR Ivan Ustyuzhaninov, Claudio Michaelis, Wieland Brendel, Matthias Bethge
2018	J	`jnl`	Robust Perception through Analysis by Synthesis. CoRR Lukas Schott, Jonas Rauber, Wieland Brendel, Matthias Bethge
2018	J	`jnl`	Trace your sources in large-scale data: one ring to find them all. CoRR Alexander Böttcher, Wieland Brendel, Bernhard Englitz, Matthias Bethge
2017	J	`jnl`	Comment on "Biologically inspired protection of deep networks from adversarial attacks". CoRR Wieland Brendel, Matthias Bethge
2017	J	`jnl`	Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models. CoRR Wieland Brendel, Jonas Rauber, Matthias Bethge
2017	J	`jnl`	Foolbox v0.8.0: A Python toolbox to benchmark the robustness of machine learning models. CoRR Jonas Rauber, Wieland Brendel, Matthias Bethge
2017	—	`conf`	What does it take to generate natural textures? ICLR (Poster) Ivan Ustyuzhaninov, Wieland Brendel, Leon A. Gatys, Matthias Bethge
2016	J	`jnl`	Texture Synthesis Using Shallow Convolutional Networks with Random Filters. CoRR Ivan Ustyuzhaninov, Wieland Brendel, Leon A. Gatys, Matthias Bethge
2014	—	`conf`	Unsupervised learning of an efficient short-term memory network. NIPS Pietro Vertechi, Wieland Brendel, Christian K. Machens
2011	—	`conf`	Demixed Principal Component Analysis. NIPS Wieland Brendel, Ranulfo Romo, Christian K. Machens