Peter Súkeník
Graduate School
Mondelli Group
Lampert Group
3 Publications
2023 | Conference Paper | IST-REx-ID: 14921 | 
P. Súkeník, M. Mondelli, and C. Lampert, “Deep neural collapse is provably optimal for the deep unconstrained features model,” in 37th Annual Conference on Neural Information Processing Systems, New Orleans, LA, United States.
[Preprint]
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2022 | Preprint | IST-REx-ID: 12662 |
P. Súkeník and C. Lampert, “Generalization in Multi-objective machine learning,” arXiv. .
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| arXiv
2022 | Conference Paper | IST-REx-ID: 12664 |
P. Súkeník, A. Kuvshinov, and S. Günnemann, “Intriguing properties of input-dependent randomized smoothing,” in Proceedings of the 39th International Conference on Machine Learning, Baltimore, MD, United States, 2022, vol. 162, pp. 20697–20743.
[Published Version]
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| Files available
| arXiv
3 Publications
2023 | Conference Paper | IST-REx-ID: 14921 |
P. Súkeník, M. Mondelli, and C. Lampert, “Deep neural collapse is provably optimal for the deep unconstrained features model,” in 37th Annual Conference on Neural Information Processing Systems, New Orleans, LA, United States.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2022 | Preprint | IST-REx-ID: 12662 |
P. Súkeník and C. Lampert, “Generalization in Multi-objective machine learning,” arXiv. .
[Preprint]
View
| DOI
| Download Preprint (ext.)
| arXiv
2022 | Conference Paper | IST-REx-ID: 12664 |
P. Súkeník, A. Kuvshinov, and S. Günnemann, “Intriguing properties of input-dependent randomized smoothing,” in Proceedings of the 39th International Conference on Machine Learning, Baltimore, MD, United States, 2022, vol. 162, pp. 20697–20743.
[Published Version]
View
| Files available
| arXiv