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2020 – today
- 2025
[j1]Peter Súkeník, Christoph Lampert:
Generalization in multi-objective machine learning. Neural Comput. Appl. 37(30): 24669-24683 (2025)- [c6]Arthur Jacot, Peter Súkeník, Zihan Wang, Marco Mondelli:
Wide Neural Networks Trained with Weight Decay Provably Exhibit Neural Collapse. ICLR 2025 - [i8]Peter Súkeník, Christoph H. Lampert, Marco Mondelli:
Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers. CoRR abs/2505.15239 (2025) - 2024
- [c5]Daniel Beaglehole, Peter Súkeník, Marco Mondelli, Mikhail Belkin:
Average gradient outer product as a mechanism for deep neural collapse. NeurIPS 2024 - [c4]Peter Súkeník, Christoph H. Lampert, Marco Mondelli:
Neural collapse vs. low-rank bias: Is deep neural collapse really optimal? NeurIPS 2024 - [i7]Daniel Beaglehole, Peter Súkeník, Marco Mondelli, Mikhail Belkin:
Average gradient outer product as a mechanism for deep neural collapse. CoRR abs/2402.13728 (2024) - [i6]Peter Súkeník, Marco Mondelli, Christoph Lampert:
Neural Collapse versus Low-rank Bias: Is Deep Neural Collapse Really Optimal? CoRR abs/2405.14468 (2024) - [i5]Arthur Jacot, Peter Súkeník, Zihan Wang, Marco Mondelli:
Wide Neural Networks Trained with Weight Decay Provably Exhibit Neural Collapse. CoRR abs/2410.04887 (2024) - 2023
- [c3]Peter Súkeník, Marco Mondelli, Christoph H. Lampert:
Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained Features Model. NeurIPS 2023 - [i4]Peter Súkeník, Marco Mondelli, Christoph Lampert:
Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained Features Model. CoRR abs/2305.13165 (2023) - 2022
- [c2]Peter Súkeník, Aleksei Kuvshinov, Stephan Günnemann:
Intriguing Properties of Input-Dependent Randomized Smoothing. ICML 2022: 20697-20743 - [c1]Peter Kocsis, Peter Súkeník, Guillem Brasó, Matthias Nießner, Laura Leal-Taixé, Ismail Elezi:
The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes. NeurIPS 2022 - [i3]Peter Súkeník, Christoph H. Lampert:
Generalization In Multi-Objective Machine Learning. CoRR abs/2208.13499 (2022) - [i2]Peter Kocsis, Peter Súkeník, Guillem Brasó, Matthias Nießner, Laura Leal-Taixé, Ismail Elezi:
The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes. CoRR abs/2210.05657 (2022) - 2021
- [i1]Peter Súkeník, Aleksei Kuvshinov, Stephan Günnemann:
Intriguing Properties of Input-dependent Randomized Smoothing. CoRR abs/2110.05365 (2021)
Coauthor Index
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