Peter Súkeník

Graduate School
Mondelli Group
Lampert Group

3 Publications

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[3]
2023 | Conference Paper | IST-REx-ID: 14921 | OA
Súkeník, Peter, et al. “Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained Features Model.” 37th Annual Conference on Neural Information Processing Systems.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[2]
2022 | Preprint | IST-REx-ID: 12662 | OA
Súkeník, Peter, and Christoph Lampert. “Generalization in Multi-Objective Machine Learning.” ArXiv, 2208.13499, doi:10.48550/arXiv.2208.13499.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[1]
2022 | Conference Paper | IST-REx-ID: 12664 | OA
Súkeník, Peter, et al. “Intriguing Properties of Input-Dependent Randomized Smoothing.” Proceedings of the 39th International Conference on Machine Learning, vol. 162, ML Research Press, 2022, pp. 20697–743.
[Published Version] View | Files available | arXiv
 
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3 Publications

Mark all

[3]
2023 | Conference Paper | IST-REx-ID: 14921 | OA
Súkeník, Peter, et al. “Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained Features Model.” 37th Annual Conference on Neural Information Processing Systems.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[2]
2022 | Preprint | IST-REx-ID: 12662 | OA
Súkeník, Peter, and Christoph Lampert. “Generalization in Multi-Objective Machine Learning.” ArXiv, 2208.13499, doi:10.48550/arXiv.2208.13499.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[1]
2022 | Conference Paper | IST-REx-ID: 12664 | OA
Súkeník, Peter, et al. “Intriguing Properties of Input-Dependent Randomized Smoothing.” Proceedings of the 39th International Conference on Machine Learning, vol. 162, ML Research Press, 2022, pp. 20697–743.
[Published Version] View | Files available | arXiv
 
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Citation Style: MLA

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