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Trieste 2019_2.jpg

PUBLICATIONS and PREPRINTS
[arXiv] 
[Google Scholar]

  • Y. Nam*, N. Fonseca*, S. H. Lee, C. Mingard, A. A. Louis, An exactly solvable model for emergence and scaling laws. ICML 2024 Workshop on High-dimensional Learning Dynamics [OpenReview] [arXiv:2404.17563]

  • N. Fonseca*, V. Guidetti*, W. Trojak*, Probing optimisation in physics-informed neural networks.

       ICLR 2023 Workshop on Physics for Machine Learning [OpenReview] [arXiv:2303.15196]

  • N. Fonseca* and V. Guidetti*, Generalizing similarity in noisy setups: the DIBS phenomenon. 

        European Conference on Artificial Intelligence 2023 [ECAI 2023] [arXiv:2201.12803]

  • N. Fonseca and E. Morgante, Probing photophobic (rel)axion dark matter. 

       [Phys. Rev. D. 103, 015011 (2021)] [arXiv: 2009.10974]

In the machine learning papers, an asterisk (*) denotes equal contribution. In theoretical high energy physics, the convention is to order the author list alphabetically.

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