David Stutz

Orcid: 0000-0002-6286-1805

According to our database1, David Stutz authored at least 29 papers between 2003 and 2024.

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Bibliography

2024
Mitigating LLM Hallucinations via Conformal Abstention.
CoRR, 2024

Capabilities of Gemini Models in Medicine.
CoRR, 2024

Conformalized Credal Set Predictors.
CoRR, 2024

On Adversarial Training without Perturbing all Examples.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023
Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators.
IEEE Trans. Pattern Anal. Mach. Intell., March, 2023

Conformal prediction under ambiguous ground truth.
Trans. Mach. Learn. Res., 2023

Unlocking Accuracy and Fairness in Differentially Private Image Classification.
CoRR, 2023

Evaluating AI systems under uncertain ground truth: a case study in dermatology.
CoRR, 2023

Robustifying Token Attention for Vision Transformers.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Certified Robust Models with Slack Control and Large Lipschitz Constants.
Proceedings of the Pattern Recognition - 45th DAGM German Conference, 2023

Improving Robustness of Vision Transformers by Reducing Sensitivity to Patch Corruptions.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
Understanding and improving robustness and uncertainty estimation in deep learning.
PhD thesis, 2022

On Fragile Features and Batch Normalization in Adversarial Training.
CoRR, 2022

Improving Corruption and Adversarial Robustness by Enhancing Weak Subnets.
CoRR, 2022

Learning Optimal Conformal Classifiers.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Improving Robustness by Enhancing Weak Subnets.
Proceedings of the Computer Vision, 2022

2021
A Closer Look at the Adversarial Robustness of Information Bottleneck Models.
CoRR, 2021

Bit Error Robustness for Energy-Efficient DNN Accelerators.
Proceedings of the Fourth Conference on Machine Learning and Systems, 2021

Relating Adversarially Robust Generalization to Flat Minima.
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020
Learning 3D Shape Completion Under Weak Supervision.
Int. J. Comput. Vis., 2020

On Mitigating Random and Adversarial Bit Errors.
CoRR, 2020

Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks.
Proceedings of the 37th International Conference on Machine Learning, 2020

Adversarial Training Against Location-Optimized Adversarial Patches.
Proceedings of the Computer Vision - ECCV 2020 Workshops, 2020

2019
Confidence-Calibrated Adversarial Training: Towards Robust Models Generalizing Beyond the Attack Used During Training.
CoRR, 2019

Disentangling Adversarial Robustness and Generalization.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018
Superpixels: An evaluation of the state-of-the-art.
Comput. Vis. Image Underst., 2018

Learning 3D Shape Completion From Laser Scan Data With Weak Supervision.
Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

2015
Superpixel Segmentation: An Evaluation.
Proceedings of the Pattern Recognition - 37th German Conference, 2015

2003
Shared source CLI essentials - exploring Microsoft's Rotor and the ECMA CLI.
O'Reilly, ISBN: 978-0-596-00351-7, 2003


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