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