Peter Súkeník
Graduate School
Mondelli Group
Lampert Group
6 Publications
2024 | Epub ahead of print | Journal Article | IST-REx-ID: 12662 | 
Súkeník, Peter, and Christoph Lampert. “Generalization in Multi-Objective Machine Learning.” Neural Computing and Applications. Springer Nature, 2024. https://doi.org/10.1007/s00521-024-10616-1.
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2024 | Published | Conference Paper | IST-REx-ID: 18890 |
Beaglehole, Daniel, Peter Súkeník, Marco Mondelli, and Mikhail Belkin. “Average Gradient Outer Product as a Mechanism for Deep Neural Collapse.” In 38th Annual Conference on Neural Information Processing Systems, Vol. 37. Neural Information Processing Systems Foundation, 2024.
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2024 | Published | Conference Paper | IST-REx-ID: 18891 |
Súkeník, Peter, Christoph Lampert, and Marco Mondelli. “Neural Collapse versus Low-Rank Bias: Is Deep Neural Collapse Really Optimal?” In 38th Annual Conference on Neural Information Processing Systems, Vol. 37. Neural Information Processing Systems Foundation, 2024.
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2023 | Published | 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, 2023.
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2022 | Published | Conference Paper | IST-REx-ID: 18876 |
Kocsis, Peter, The unreasonable effectiveness of fully-connected layers for low-data regimes. 36th Conference on Neural Information Processing Systems 35. 2022
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2022 | Published | Conference Paper | IST-REx-ID: 12664 |
Súkeník, Peter, Intriguing properties of input-dependent randomized smoothing. Proceedings of the 39th International Conference on Machine Learning 162. 2022
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6 Publications
2024 | Epub ahead of print | Journal Article | IST-REx-ID: 12662 |
Súkeník, Peter, and Christoph Lampert. “Generalization in Multi-Objective Machine Learning.” Neural Computing and Applications. Springer Nature, 2024. https://doi.org/10.1007/s00521-024-10616-1.
[Published Version]
View
| DOI
| Download Published Version (ext.)
| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18890 |
Beaglehole, Daniel, Peter Súkeník, Marco Mondelli, and Mikhail Belkin. “Average Gradient Outer Product as a Mechanism for Deep Neural Collapse.” In 38th Annual Conference on Neural Information Processing Systems, Vol. 37. Neural Information Processing Systems Foundation, 2024.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18891 |
Súkeník, Peter, Christoph Lampert, and Marco Mondelli. “Neural Collapse versus Low-Rank Bias: Is Deep Neural Collapse Really Optimal?” In 38th Annual Conference on Neural Information Processing Systems, Vol. 37. Neural Information Processing Systems Foundation, 2024.
[Published Version]
View
| Files available
2023 | Published | 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, 2023.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 18876 |
Kocsis, Peter, The unreasonable effectiveness of fully-connected layers for low-data regimes. 36th Conference on Neural Information Processing Systems 35. 2022
[Published Version]
View
| Files available
| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 12664 |
Súkeník, Peter, Intriguing properties of input-dependent randomized smoothing. Proceedings of the 39th International Conference on Machine Learning 162. 2022
[Published Version]
View
| Files available
| arXiv