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Wieland Brendel

  1. Shortcut learning in deep neural networks
    2020/04/16 by Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis +5 · 2 voices · 195 citations
    Computer Science · #Adversarial Robustness in Machine Learning #Anomaly Detection Techniques and Applications #Machine Learning and Data Classification #cs.AI #cs.CV #cs.LG #q-bio.NC
  2. ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
    2018/11/29 by Robert Geirhos, Geirhos, Robert, Patricia Rubisch +9 · 98 citations
    Neuroscience · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Biological sciences #FOS: Computer and information sciences #Face Recognition and Perception #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neurons and Cognition (q-bio.NC)
  3. Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models
    2017/12/12 by Wieland Brendel, Brendel, Wieland, Jonas Rauber +3 · 127 citations
    Computer Science · #Adversarial Robustness in Machine Learning #Anomaly Detection Techniques and Applications #Advanced Neural Network Applications
  4. On Evaluating Adversarial Robustness
    2019/02/18 by Nicholas Carlini, Anish Athalye, Carlini, Nicholas +15 · 75 citations
    Biochemistry, Genetics and Molecular Biology · Computer Science · #Advanced Malware Detection Techniques #Adversarial Robustness in Machine Learning #Bacillus and Francisella bacterial research #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  5. Improving robustness against common corruptions by covariate shift adaptation
    2020/06/30 by Steffen Schneider, Schneider, Steffen, Evgenia Rusak +9 · 39 citations
    Computer Science · #Domain Adaptation and Few-Shot Learning #Adversarial Robustness in Machine Learning #Advanced Neural Network Applications
  6. Self-Supervised Learning with Data Augmentations Provably Isolates\n Content from Style
    2021/06/08 by Julius von Kügelgen, von Kügelgen, Julius, Yash Sharma +11 · 30 citations
    Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Image Processing Techniques and Applications #Image Processing and 3D Reconstruction #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  7. On Adaptive Attacks to Adversarial Example Defenses
    2020/02/19 by Florian Tramèr, Nicholas Carlini, Tramer, Florian +5 · 30 citations
    Computer Science · #Advanced Malware Detection Techniques #Adversarial Robustness in Machine Learning #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Security and Verification in Computing
  8. Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet
    2019/03/20 by Wieland Brendel, Matthias Bethge, Brendel, Wieland +1 · 1 voice · 17 citations
    Computer Science · Engineering · #Advanced Neural Network Applications #Domain Adaptation and Few-Shot Learning #Industrial Vision Systems and Defect Detection #cs.CV #cs.LG #stat.ML
  9. Contrastive Learning Inverts the Data Generating Process
    2021/02/17 by R. Zimmermann, Yash Sharma, Zimmermann, Roland S. +7 · 21 citations
    Computer Science · #Domain Adaptation and Few-Shot Learning #Generative Adversarial Networks and Image Synthesis #Music and Audio Processing
  10. Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse\n Coding
    2020/07/21 by David Klindt, Lukas Schott, Klindt, David +11 · 13 citations
    Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #Digital Media Forensic Detection #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Image and Signal Denoising Methods #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  11. Partial success in closing the gap between human and machine vision
    2021/06/14 by Robert Geirhos, Kantharaju Narayanappa, Geirhos, Robert +11 · 12 citations
    Computer Science · Neuroscience · #Adversarial Robustness in Machine Learning #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Biological sciences #FOS: Computer and information sciences #Face Recognition and Perception #Machine Learning (cs.LG) #Neurons and Cognition (q-bio.NC)
  12. Provably Learning Object-Centric Representations
    2023/05/23 by Jack Brady, R. Zimmermann, Brady, Jack +9 · 12 citations
    Computer Science · #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Multimodal Machine Learning Applications
  13. InfoNCE: Identifying the Gap Between Theory and Practice
    2024/06/28 by Evgenia Rusak, Rusak, Evgenia, Patrik Reizinger +9 · 11 citations
    Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Face recognition and analysis #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  14. Don't trust your eyes: on the (un)reliability of feature visualizations
    2023/06/07 by Robert Geirhos, Geirhos, Robert, R. Zimmermann +8 · 1 voice · 5 citations
    Biochemistry, Genetics and Molecular Biology · Computer Science · #Cell Image Analysis Techniques #Explainable Artificial Intelligence (XAI) #Neural Networks and Applications
  15. Does CLIP's Generalization Performance Mainly Stem from High Train-Test Similarity?
    2023/10/14 by P. Mayilvahanan, Thaddäus Wiedemer, Mayilvahanan, Prasanna +7 · 1 voice · 7 citations
    Computer Science · Medicine · #Artificial Intelligence (cs.AI) #COVID-19 diagnosis using AI #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Healthcare
  16. Interaction Asymmetry: A General Principle for Learning Composable Abstractions
    2024/11/12 by Jack Brady, Brady, Jack, Julius von Kügelgen +9 · 9 citations
    Computer Science · #Logic, Reasoning, and Knowledge #Intelligent Tutoring Systems and Adaptive Learning #Semantic Web and Ontologies
  17. Cross-Entropy Is All You Need To Invert the Data Generating Process
    2024/10/29 by Patrik Reizinger, Reizinger, Patrik, Alice Bizeul +11 · 10 citations
    Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications
  18. Embrace the Gap: VAEs Perform Independent Mechanism Analysis
    2022/06/06 by Patrik Reizinger, Luigi Gresele, Reizinger, Patrik +15 · 4 citations
    Physics and Astronomy · Computer Science · Biochemistry, Genetics and Molecular Biology · #Model Reduction and Neural Networks #Domain Adaptation and Few-Shot Learning #Protein Structure and Dynamics
  19. How Well do Feature Visualizations Support Causal Understanding of CNN Activations?
    2021/06/23 by R. Zimmermann, Zimmermann, Roland S., Judy Borowski +9 · 3 citations
    Computer Science · Biochemistry, Genetics and Molecular Biology · #Explainable Artificial Intelligence (XAI) #Adversarial Robustness in Machine Learning #Cell Image Analysis Techniques
  20. Scale Alone Does not Improve Mechanistic Interpretability in Vision Models
    2023/07/11 by R. Zimmermann, Zimmermann, Roland S., Thomas Klein +3 · 4 citations
    Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences
  21. Foolbox: A Python toolbox to benchmark the robustness of machine learning models
    2017/07/13 by Jonas Rauber, Wieland Brendel, Rauber, Jonas +3 · 2 citations
    Computer Science · #Computational Physics and Python Applications #Machine Learning and Data Classification
  22. In Search of Forgotten Domain Generalization
    2024/10/10 by P. Mayilvahanan, R. Zimmermann, Mayilvahanan, Prasanna +11 · 6 citations
    Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Reservoir Engineering and Simulation Methods
  23. Position: Understanding LLMs Requires More Than Statistical Generalization
    2024/05/03 by Patrik Reizinger, Reizinger, Patrik, Szilvia Ujváry +9 · 2 voices · 3 citations
    Computer Science · Mathematics · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.LG #stat.ML
  24. Learning From Brains How to Regularize Machines
    2019/11/11 by Zhe Li, Li, Zhe, Wieland Brendel +17 · 1 voice · 1 citation
    Biochemistry, Genetics and Molecular Biology · Computer Science · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Neurons and Cognition (q-bio.NC) #cs.AI #cs.CV #cs.LG #q-bio.NC
  25. LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws
    2025/02/17 by P. Mayilvahanan, Mayilvahanan, Prasanna, Thaddäus Wiedemer +6 · 7 citations
    Business, Management and Accounting · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #FinTech, Crowdfunding, Digital Finance #Machine Learning (cs.LG)
  26. EagerPy: Writing Code That Works Natively with PyTorch, TensorFlow, JAX,\n and NumPy
    2020/08/10 by Jonas Rauber, Matthias Bethge, Rauber, Jonas +3 · 2 citations
    Computer Science · #Computational Physics and Python Applications #Software Engineering Research
  27. Trace your sources in large-scale data: one ring to find them all
    2018/03/23 by Alexander Böttcher, Wieland Brendel, Böttcher, Alexander +5 · 1 citation
    Chemistry · Computer Science · #Blind Source Separation Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Methodology (stat.ME) #Spectroscopy and Chemometric Analyses #Speech and Audio Processing
  28. On the surprising similarities between supervised and self-supervised models
    2020/10/16 by Robert Geirhos, Geirhos, Robert, Kantharaju Narayanappa +9 · 3 citations
    Computer Science · #Domain Adaptation and Few-Shot Learning #Machine Learning and Data Classification #Topic Modeling
  29. Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints
    2021/02/25 by Maura Pintor, Fabio Roli, Pintor, Maura +5 · 2 citations
    Computer Science · #Advanced Neural Network Applications #Adversarial Robustness in Machine Learning #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Algorithms
  30. Accurate, reliable and fast robustness evaluation
    2019/07/01 by Wieland Brendel, Jonas Rauber, Brendel, Wieland +7 · 1 citation
    Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · #Adversarial Robustness in Machine Learning #Computer Vision and Pattern Recognition (cs.CV) #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Integrated Circuits and Semiconductor Failure Analysis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural and Evolutionary Computing (cs.NE) #Spectroscopy Techniques in Biomedical and Chemical Research
  31. Local Convolutions Cause an Implicit Bias towards High Frequency Adversarial Examples
    2020/06/19 by Josue Ortega, Yilong Ju, Caro, Josue Ortega +11 · 1 citation
    Computer Science · Engineering · Materials Science · #Adversarial Robustness in Machine Learning #Advancements in Semiconductor Devices and Circuit Design #Machine Learning in Materials Science
  32. Rule Extrapolation in Language Models: A Study of Compositional Generalization on OOD Prompts
    2024/09/09 by Anna Mészáros, Szilvia Ujváry, Mészáros, Anna +7 · 1 voice · 2 citations
    Computer Science · Mathematics · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Natural Language Processing Techniques #cs.CL #cs.LG #stat.ML
  33. Is Generation Required for Data-Efficient Perception?
    2025/12/09 by Jack Brady, Brady, Jack, Bernhard Schölkopf +7 · 1 voice
    Computer Science · Neuroscience · #Generative Adversarial Networks and Image Synthesis #Face Recognition and Perception #Visual perception and processing mechanisms
  34. Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research
    2025/04/17 by Patrik Reizinger, Reizinger, Patrik, Randall Balestriero +5 · 1 voice · 1 citation
    Social Sciences · Psychology · #cs.LG #cs.AI #stat.ML
  35. An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis
    2023/11/29 by Goutham Rajendran, Rajendran, Goutham, Patrik Reizinger +5 · 1 citation
    Computer Science · #Blind Source Separation Techniques #Neural Networks and Applications #Gaussian Processes and Bayesian Inference
  36. HALLMARK: Diagnosing Three Failure Modes in LLM Citation Verifiers
    2026/07/20 by Patrik Reizinger, Wieland Brendel
    #cs.CR #cs.AI #cs.LG