Peter Súkeník

7 Publications

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[7]
2025 | Published | Conference Paper | IST-REx-ID: 20035 | OA
Jacot, Arthur, et al. “Wide Neural Networks Trained with Weight Decay Provably Exhibit Neural Collapse.” 13th International Conference on Learning Representations, ICLR, 2025, pp. 1905–31.
[Published Version] View | Files available | arXiv
 
[6]
2024 | Epub ahead of print | Journal Article | IST-REx-ID: 12662 | OA
Súkeník, Peter, and Christoph Lampert. “Generalization in Multi-Objective Machine Learning.” Neural Computing and Applications, Springer Nature, 2024, doi:10.1007/s00521-024-10616-1.
[Published Version] View | DOI | Download Published Version (ext.) | arXiv
 
[5]
2024 | Published | Conference Paper | IST-REx-ID: 18890 | OA
Beaglehole, Daniel, et al. “Average Gradient Outer Product as a Mechanism for Deep Neural Collapse.” 38th Annual Conference on Neural Information Processing Systems, vol. 37, Neural Information Processing Systems Foundation, 2024.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[4]
2024 | Published | Conference Paper | IST-REx-ID: 18891 | OA
Súkeník, Peter, et al. “Neural Collapse versus Low-Rank Bias: Is Deep Neural Collapse Really Optimal?” 38th Annual Conference on Neural Information Processing Systems, vol. 37, Neural Information Processing Systems Foundation, 2024.
[Published Version] View | Files available | arXiv
 
[3]
2023 | Published | 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, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[2]
2022 | Published | 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
 
[1]
2022 | Published | Conference Paper | IST-REx-ID: 18876 | OA
Kocsis, Peter, et al. “The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes.” 36th Conference on Neural Information Processing Systems, vol. 35, Neural Information Processing Systems Foundation, 2022, pp. 1896–908.
[Published Version] View | Files available | arXiv
 
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7 Publications

Mark all

[7]
2025 | Published | Conference Paper | IST-REx-ID: 20035 | OA
Jacot, Arthur, et al. “Wide Neural Networks Trained with Weight Decay Provably Exhibit Neural Collapse.” 13th International Conference on Learning Representations, ICLR, 2025, pp. 1905–31.
[Published Version] View | Files available | arXiv
 
[6]
2024 | Epub ahead of print | Journal Article | IST-REx-ID: 12662 | OA
Súkeník, Peter, and Christoph Lampert. “Generalization in Multi-Objective Machine Learning.” Neural Computing and Applications, Springer Nature, 2024, doi:10.1007/s00521-024-10616-1.
[Published Version] View | DOI | Download Published Version (ext.) | arXiv
 
[5]
2024 | Published | Conference Paper | IST-REx-ID: 18890 | OA
Beaglehole, Daniel, et al. “Average Gradient Outer Product as a Mechanism for Deep Neural Collapse.” 38th Annual Conference on Neural Information Processing Systems, vol. 37, Neural Information Processing Systems Foundation, 2024.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[4]
2024 | Published | Conference Paper | IST-REx-ID: 18891 | OA
Súkeník, Peter, et al. “Neural Collapse versus Low-Rank Bias: Is Deep Neural Collapse Really Optimal?” 38th Annual Conference on Neural Information Processing Systems, vol. 37, Neural Information Processing Systems Foundation, 2024.
[Published Version] View | Files available | arXiv
 
[3]
2023 | Published | 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, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[2]
2022 | Published | 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
 
[1]
2022 | Published | Conference Paper | IST-REx-ID: 18876 | OA
Kocsis, Peter, et al. “The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes.” 36th Conference on Neural Information Processing Systems, vol. 35, Neural Information Processing Systems Foundation, 2022, pp. 1896–908.
[Published Version] View | Files available | arXiv
 
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