Peter Súkeník

Graduate School
Mondelli Group
Lampert Group

3 Publications

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[3]
2023 | Conference Paper | IST-REx-ID: 14921 | OA
Súkeník P, Mondelli M, Lampert C. Deep neural collapse is provably optimal for the deep unconstrained features model. In: 37th Annual Conference on Neural Information Processing Systems.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[2]
2022 | Preprint | IST-REx-ID: 12662 | OA
Súkeník P, Lampert C. Generalization in Multi-objective machine learning. arXiv. doi:10.48550/arXiv.2208.13499
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[1]
2022 | Conference Paper | IST-REx-ID: 12664 | OA
Súkeník P, Kuvshinov A, Günnemann S. Intriguing properties of input-dependent randomized smoothing. In: Proceedings of the 39th International Conference on Machine Learning. Vol 162. ML Research Press; 2022:20697-20743.
[Published Version] View | Files available | arXiv
 
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3 Publications

Mark all

[3]
2023 | Conference Paper | IST-REx-ID: 14921 | OA
Súkeník P, Mondelli M, Lampert C. Deep neural collapse is provably optimal for the deep unconstrained features model. In: 37th Annual Conference on Neural Information Processing Systems.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[2]
2022 | Preprint | IST-REx-ID: 12662 | OA
Súkeník P, Lampert C. Generalization in Multi-objective machine learning. arXiv. doi:10.48550/arXiv.2208.13499
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[1]
2022 | Conference Paper | IST-REx-ID: 12664 | OA
Súkeník P, Kuvshinov A, Günnemann S. Intriguing properties of input-dependent randomized smoothing. In: Proceedings of the 39th International Conference on Machine Learning. Vol 162. ML Research Press; 2022:20697-20743.
[Published Version] View | Files available | arXiv
 
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Citation Style: AMA

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