This repository contains the experimental source code to reproduce the numerical experiments in:
- J. Chirinos Rodriguez, E. De Vito, C. Molinari, L. Rosasco, S. Villa. A Supervised Learning Approach to Regularization of Inverse Problems. 2023. Arxiv preprint
The numerical experiments exposed in section 6 are divided in 4 documents: spectral.ipynb, sig_denoising.ipynb, sig_deblurring.ipynb and TV_denoising.ipynb.
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This project has ben supported by the TraDE-OPT project, with JCR and SV as members, and which received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 861137. -
LR acknowledges the financial support of the European Research Council (grant SLING 819789), the AFOSR projects FA9550-18-1-7009 (European Office of Aerospace Research and Development), the EU H2020-MSCA-RISE project NoMADS - DLV-777826, and the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216. SV and LR acknowledge the support of the AFOSR project FA8655-22-1-7034. The research by EDV, SV and CM has been supported by the MIUR Excellence Department Project awarded to Dipartimento di Matematica, Università di Genova, CUP D33C23001110001. EDV, SV and CM are also members of the Gruppo Nazionale per l’Analisi Matematica, la Probabilità e le loro Applicazioni (GNAMPA) of the Istituto Nazionale di Alta Matematica (INdAM). -
All other data used for numerical experiments in this project have been created artificially by the authors.
This project is licensed under the GPLv3 license - see LICENSE for details.