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Umberto M. Tomasini

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2024
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Umberto M. Tomasini, Matthieu Wyart:
How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model. ICML 2024
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  - journals/corr/abs-2404-10727
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Umberto M. Tomasini, Matthieu Wyart:
How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model. CoRR abs/2404.10727 (2024)
2023
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Umberto M. Tomasini, Leonardo Petrini, Francesco Cagnetta, Matthieu Wyart:
How deep convolutional neural networks lose spatial information with training. Mach. Learn. Sci. Technol. 4(4): 45026 (2023)
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  - journals/corr/abs-2307-02129
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Leonardo Petrini, Francesco Cagnetta, Umberto M. Tomasini, Alessandro Favero, Matthieu Wyart:
How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model. CoRR abs/2307.02129 (2023)
2022
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Umberto M. Tomasini, Antonio Sclocchi, Matthieu Wyart:
Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data. ICML 2022: 21548-21583
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  - journals/corr/abs-2202-03348
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Umberto M. Tomasini, Antonio Sclocchi, Matthieu Wyart:
Failure and success of the spectral bias prediction for Kernel Ridge Regression: the case of low-dimensional data. CoRR abs/2202.03348 (2022)
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  - journals/corr/abs-2210-01506
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Umberto M. Tomasini, Leonardo Petrini, Francesco Cagnetta, Matthieu Wyart:
How deep convolutional neural networks lose spatial information with training. CoRR abs/2210.01506 (2022)

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