MSML21: Mathematical and Scientific Machine Learning

[dmm]

Starts in 5 minutes:

msml21.github.io/

No registration is needed - they are just handling it in a relaxed fashion

Comments

[dmm]

"Inverse problems": https://msml21.github.io/session5/

Interpretable and Learnable Super-Resolution Time-Frequency Representation, Randall Balestriero (Rice University), Herve Glotin (); Richard Baraniuk (Rice University)

Paper Highlight, by Dennis Elbrachter

The paper introduces a method of obtaining super-resolved quadratic time-frequency representations via Gaussian filtering of the Wigner-Ville transform. It is both interpretable as well as computationally feasible, achieving state-of-the-art results on various datasets. I particularly enjoyed the clean presentation of formal results augmented by helpful explanations of the intuitions behind them.

https://en.wikipedia.org/wiki/Chirplet_transform


Phase Retrieval with Holography and Untrained Priors: Tackling the Challenges of Low-Photon Nanoscale Imaging, Hannah Lawrence (Flatiron Institute); David Barmherzig (); Henry Li (Yale); Michael Eickenberg (UC Berkeley); Marylou Gabrié (NYU / Flatiron Institute)

Paper Highlight, by Reinhard Heckel

The paper introduces a novel dataset-free deep learning framework for holographic phase retrieval. It shows, in a realistic simulation setups, that un-trained neural network enable to regularize holographic phase retrieval. It thus shows that non-linear inverse problems can be regularized with neural networks without any training, thereby making an important contribution in the intersection of machine learning and inverse problems.