Wide neural networks trained with weight decay provably exhibit neural collapse
A. Jacot, P. Súkeník, Z. Wang, M. Mondelli, in:, 13th International Conference on Learning Representations, ICLR, 2025, pp. 1905–1931.

Generalization in multi-objective machine learning
P. Súkeník, C. Lampert, Neural Computing and Applications (2024).

Average gradient outer product as a mechanism for deep neural collapse
D. Beaglehole, P. Súkeník, M. Mondelli, M. Belkin, in:, 38th Annual Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024.

Neural collapse versus low-rank bias: Is deep neural collapse really optimal?
P. Súkeník, C. Lampert, M. Mondelli, in:, 38th Annual Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024.

Deep neural collapse is provably optimal for the deep unconstrained features model
P. Súkeník, M. Mondelli, C. Lampert, in:, 37th Annual Conference on Neural Information Processing Systems, 2023.

Intriguing properties of input-dependent randomized smoothing
P. Súkeník, A. Kuvshinov, S. Günnemann, in:, Proceedings of the 39th International Conference on Machine Learning, ML Research Press, 2022, pp. 20697–20743.

The unreasonable effectiveness of fully-connected layers for low-data regimes
P. Kocsis, P. Súkeník, G. Brasó, M. Niessner, L. Leal-Taixé, I. Elezi, in:, 36th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2022, pp. 1896–1908.