Peter Súkeník

7 Publications

Mark all

[7]
2025 | Published | Conference Paper | IST-REx-ID: 20035 | OA
Jacot, A., Súkeník, P., Wang, Z., & Mondelli, M. (2025). Wide neural networks trained with weight decay provably exhibit neural collapse. In 13th International Conference on Learning Representations (pp. 1905–1931). Singapore, Singapore: ICLR.
[Published Version] View | Files available | arXiv
 
[6]
2024 | Epub ahead of print | Journal Article | IST-REx-ID: 12662 | OA
Súkeník, P., & Lampert, C. (2024). Generalization in multi-objective machine learning. Neural Computing and Applications. Springer Nature. https://doi.org/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, D., Súkeník, P., Mondelli, M., & Belkin, M. (2024). Average gradient outer product as a mechanism for deep neural collapse. In 38th Annual Conference on Neural Information Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[4]
2024 | Published | Conference Paper | IST-REx-ID: 18891 | OA
Súkeník, P., Lampert, C., & Mondelli, M. (2024). Neural collapse versus low-rank bias: Is deep neural collapse really optimal? In 38th Annual Conference on Neural Information Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.
[Published Version] View | Files available | arXiv
 
[3]
2023 | Published | Conference Paper | IST-REx-ID: 14921 | OA
Súkeník, P., Mondelli, M., & Lampert, C. (2023). Deep neural collapse is provably optimal for the deep unconstrained features model. In 37th Annual Conference on Neural Information Processing Systems. New Orleans, LA, United States.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[2]
2022 | Published | Conference Paper | IST-REx-ID: 12664 | OA
Súkeník, P., Kuvshinov, A., & Günnemann, S. (2022). Intriguing properties of input-dependent randomized smoothing. In Proceedings of the 39th International Conference on Machine Learning (Vol. 162, pp. 20697–20743). Baltimore, MD, United States: ML Research Press.
[Published Version] View | Files available | arXiv
 
[1]
2022 | Published | Conference Paper | IST-REx-ID: 18876 | OA
Kocsis, P., Súkeník, P., Brasó, G., Niessner, M., Leal-Taixé, L., & Elezi, I. (2022). The unreasonable effectiveness of fully-connected layers for low-data regimes. In 36th Conference on Neural Information Processing Systems (Vol. 35, pp. 1896–1908). New Orleans, LA, United States: Neural Information Processing Systems Foundation.
[Published Version] View | Files available | arXiv
 
Marked Publication(s)

Search

Filter Publications

Display / Sort

Citation Style: APA

Export / Embed

Grants


7 Publications

Mark all

[7]
2025 | Published | Conference Paper | IST-REx-ID: 20035 | OA
Jacot, A., Súkeník, P., Wang, Z., & Mondelli, M. (2025). Wide neural networks trained with weight decay provably exhibit neural collapse. In 13th International Conference on Learning Representations (pp. 1905–1931). Singapore, Singapore: ICLR.
[Published Version] View | Files available | arXiv
 
[6]
2024 | Epub ahead of print | Journal Article | IST-REx-ID: 12662 | OA
Súkeník, P., & Lampert, C. (2024). Generalization in multi-objective machine learning. Neural Computing and Applications. Springer Nature. https://doi.org/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, D., Súkeník, P., Mondelli, M., & Belkin, M. (2024). Average gradient outer product as a mechanism for deep neural collapse. In 38th Annual Conference on Neural Information Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[4]
2024 | Published | Conference Paper | IST-REx-ID: 18891 | OA
Súkeník, P., Lampert, C., & Mondelli, M. (2024). Neural collapse versus low-rank bias: Is deep neural collapse really optimal? In 38th Annual Conference on Neural Information Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.
[Published Version] View | Files available | arXiv
 
[3]
2023 | Published | Conference Paper | IST-REx-ID: 14921 | OA
Súkeník, P., Mondelli, M., & Lampert, C. (2023). Deep neural collapse is provably optimal for the deep unconstrained features model. In 37th Annual Conference on Neural Information Processing Systems. New Orleans, LA, United States.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[2]
2022 | Published | Conference Paper | IST-REx-ID: 12664 | OA
Súkeník, P., Kuvshinov, A., & Günnemann, S. (2022). Intriguing properties of input-dependent randomized smoothing. In Proceedings of the 39th International Conference on Machine Learning (Vol. 162, pp. 20697–20743). Baltimore, MD, United States: ML Research Press.
[Published Version] View | Files available | arXiv
 
[1]
2022 | Published | Conference Paper | IST-REx-ID: 18876 | OA
Kocsis, P., Súkeník, P., Brasó, G., Niessner, M., Leal-Taixé, L., & Elezi, I. (2022). The unreasonable effectiveness of fully-connected layers for low-data regimes. In 36th Conference on Neural Information Processing Systems (Vol. 35, pp. 1896–1908). New Orleans, LA, United States: Neural Information Processing Systems Foundation.
[Published Version] View | Files available | arXiv
 
Marked Publication(s)

Search

Filter Publications

Display / Sort

Citation Style: APA

Export / Embed