David Stutz
Google (United States)(US)Google DeepMind (United Kingdom)(GB)Google (United Kingdom)(GB)
Publications by Year
Research Areas
Adversarial Robustness in Machine Learning, Anomaly Detection Techniques and Applications, Advanced Neural Network Applications, Domain Adaptation and Few-Shot Learning, Integrated Circuits and Semiconductor Failure Analysis
Most-Cited Works
- → Superpixels: An evaluation of the state-of-the-art(2017)522 cited
- → Learning 3D Shape Completion from Laser Scan Data with Weak Supervision(2018)246 cited
- → Capabilities of Gemini Models in Medicine(2024)89 cited
- Understanding Convolutional Neural Networks(2014)
- Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks(2020)
- → Superpixel Segmentation: An Evaluation(2015)33 cited
- → Relating Adversarially Robust Generalization to Flat Minima(2021)28 cited
- → Robustifying Token Attention for Vision Transformers(2023)26 cited