Bernd Prach
5 Publications
2025 | Published | Thesis | IST-REx-ID: 19759 | 
Prach, B. (2025). Robust image classification with 1-Lipschitz networks. Institute of Science and Technology Austria. https://doi.org/10.15479/10.15479/at-ista-19759
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2024 | Draft | Preprint | IST-REx-ID: 18874 |
Prach, B., & Lampert, C. (n.d.). Intriguing properties of robust classification. arXiv. https://doi.org/10.48550/arXiv.2412.04245
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2024 | Published | Conference Paper | IST-REx-ID: 17426 |
Prach, B., Brau, F., Buttazzo, G., & Lampert, C. (2024). 1-Lipschitz layers compared: Memory, speed, and certifiable robustness. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 24574–24583). Seattle, WA, United States: Computer Vision Foundation. https://doi.org/10.1109/CVPR52733.2024.02320
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2023 | Draft | Preprint | IST-REx-ID: 15039 |
Prach, B., & Lampert, C. (n.d.). 1-Lipschitz neural networks are more expressive with N-activations. arXiv. https://doi.org/10.48550/ARXIV.2311.06103
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2022 | Published | Conference Paper | IST-REx-ID: 11839 |
Prach, B., & Lampert, C. (2022). Almost-orthogonal layers for efficient general-purpose Lipschitz networks. In Computer Vision – ECCV 2022 (Vol. 13681, pp. 350–365). Tel Aviv, Israel: Springer Nature. https://doi.org/10.1007/978-3-031-19803-8_21
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5 Publications
2025 | Published | Thesis | IST-REx-ID: 19759 |
Prach, B. (2025). Robust image classification with 1-Lipschitz networks. Institute of Science and Technology Austria. https://doi.org/10.15479/10.15479/at-ista-19759
[Published Version]
View
| Files available
| DOI
2024 | Draft | Preprint | IST-REx-ID: 18874 |
Prach, B., & Lampert, C. (n.d.). Intriguing properties of robust classification. arXiv. https://doi.org/10.48550/arXiv.2412.04245
[Preprint]
View
| Files available
| DOI
| Download Preprint (ext.)
| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 17426 |
Prach, B., Brau, F., Buttazzo, G., & Lampert, C. (2024). 1-Lipschitz layers compared: Memory, speed, and certifiable robustness. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 24574–24583). Seattle, WA, United States: Computer Vision Foundation. https://doi.org/10.1109/CVPR52733.2024.02320
[Preprint]
View
| Files available
| DOI
| Download Preprint (ext.)
| WoS
| arXiv
2023 | Draft | Preprint | IST-REx-ID: 15039 |
Prach, B., & Lampert, C. (n.d.). 1-Lipschitz neural networks are more expressive with N-activations. arXiv. https://doi.org/10.48550/ARXIV.2311.06103
[Preprint]
View
| Files available
| DOI
| Download Preprint (ext.)
| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 11839 |
Prach, B., & Lampert, C. (2022). Almost-orthogonal layers for efficient general-purpose Lipschitz networks. In Computer Vision – ECCV 2022 (Vol. 13681, pp. 350–365). Tel Aviv, Israel: Springer Nature. https://doi.org/10.1007/978-3-031-19803-8_21
[Preprint]
View
| Files available
| DOI
| Download Preprint (ext.)
| WoS
| arXiv