Wieland Brendel
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
26
Venues
5
Active years
2017–2025
Best venue rank
A*
Where they publish
Papers
26 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | AISTATS | InfoNCE: Identifying the Gap Between Theory and Practice. | Evgenia Rusak, Patrik Reizinger, Attila Juhos, Oliver Bringmann, Roland S. Zimmermann, Wieland Brendel |
| 2025 | ICCV | VGGSounder: Audio-Visual Evaluations for Foundation Models. | Daniil Zverev, Thaddus Wiedemer, Ameya Prabhu, Matthias Bethge, Wieland Brendel, A. Sophia Koepke |
| 2025 | ICLR | Interaction Asymmetry: A General Principle for Learning Composable Abstractions. | Jack Brady, Julius von Kgelgen, Sbastien Lachapelle, Simon Buchholz, Thomas Kipf, Wieland Brendel |
| 2025 | ICLR | In Search of Forgotten Domain Generalization. | Prasanna Mayilvahanan, Roland S. Zimmermann, Thaddus Wiedemer, Evgenia Rusak, Attila Juhos, Matthias Bethge, Wieland Brendel |
| 2025 | ICLR | Cross-Entropy Is All You Need To Invert the Data Generating Process. | Patrik Reizinger, Alice Bizeul, Attila Juhos, Julia E. Vogt, Randall Balestriero, Wieland Brendel, David A. Klindt |
| 2025 | ICLR | Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning. | Patrik Reizinger, Siyuan Guo, Ferenc Huszr, Bernhard Schlkopf, Wieland Brendel |
| 2025 | ICML | LAION-C: An Out-of-Distribution Benchmark for Web-Scale Vision Models. | Fanfei Li, Thomas Klein, Wieland Brendel, Robert Geirhos, Roland S. Zimmermann |
| 2025 | ICML | LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws. | Prasanna Mayilvahanan, Thaddus Wiedemer, Sayak Mallick, Matthias Bethge, Wieland Brendel |
| 2025 | ICML | Position: An Empirically Grounded Identifiability Theory Will Accelerate Self Supervised Learning Research. | Patrik Reizinger, Randall Balestriero, David A. Klindt, Wieland Brendel |
| 2024 | ICLR | Effective pruning of web-scale datasets based on complexity of concept clusters. | Amro Abbas, Evgenia Rusak, Kushal Tirumala, Wieland Brendel, Kamalika Chaudhuri, Ari S. Morcos |
| 2024 | ICLR | Does CLIP's generalization performance mainly stem from high train-test similarity? | Prasanna Mayilvahanan, Thaddus Wiedemer, Evgenia Rusak, Matthias Bethge, Wieland Brendel |
| 2024 | ICLR | Provable Compositional Generalization for Object-Centric Learning. | Thaddus Wiedemer, Jack Brady, Alexander Panfilov, Attila Juhos, Matthias Bethge, Wieland Brendel |
| 2024 | ICML | Don't trust your eyes: on the (un)reliability of feature visualizations. | Robert Geirhos, Roland S. Zimmermann, Blair L. Bilodeau, Wieland Brendel, Been Kim |
| 2024 | ICML | Position: Understanding LLMs Requires More Than Statistical Generalization. | Patrik Reizinger, Szilvia Ujvry, Anna Mszros, Anna Kerekes, Wieland Brendel, Ferenc Huszr |
| 2023 | ICLR | Iterative weakly supervised learning for novel class object detection. | Dejana Mandic, Wieland Brendel, Claudio Michaelis |
| 2023 | ICML | Provably Learning Object-Centric Representations. | Jack Brady, Roland S. Zimmermann, Yash Sharma, Bernhard Schlkopf, Julius von Kgelgen, Wieland Brendel |
| 2022 | ICLR | Visual Representation Learning Does Not Generalize Strongly Within the Same Domain. | Lukas Schott, Julius von Kgelgen, Frederik Truble, Peter Vincent Gehler, Chris Russell, Matthias Bethge, Bernhard Schlkopf, Francesco Locatello, Wieland Brendel |
| 2021 | ICLR | Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization. | Judy Borowski, Roland Simon Zimmermann, Judith Schepers, Robert Geirhos, Thomas S. A. Wallis, Matthias Bethge, Wieland Brendel |
| 2021 | ICLR | Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding. | David A. Klindt, Lukas Schott, Yash Sharma, Ivan Ustyuzhaninov, Wieland Brendel, Matthias Bethge, Dylan M. Paiton |
| 2021 | ICML | Contrastive Learning Inverts the Data Generating Process. | Roland S. Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, Wieland Brendel |
| 2020 | ECCV | A Simple Way to Make Neural Networks Robust Against Diverse Image Corruptions. | Evgenia Rusak, Lukas Schott, Roland S. Zimmermann, Julian Bitterwolf, Oliver Bringmann, Matthias Bethge, Wieland Brendel |
| 2019 | ICLR | Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet. | Wieland Brendel, Matthias Bethge |
| 2019 | ICLR | ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. | Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, Wieland Brendel |
| 2019 | ICLR | Towards the first adversarially robust neural network model on MNIST. | Lukas Schott, Jonas Rauber, Matthias Bethge, Wieland Brendel |
| 2018 | ICLR | Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models. | Wieland Brendel, Jonas Rauber, Matthias Bethge |
| 2017 | ICLR | What does it take to generate natural textures? | Ivan Ustyuzhaninov, Wieland Brendel, Leon A. Gatys, Matthias Bethge |