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.
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.
no subject
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.