no code implementations • 5 Aug 2021 • Pawan Bharadwaj, Matthew Li, Laurent Demanet
This paper considers physical systems described by hidden states and indirectly observed through repeated measurements corrupted by unmodeled nuisance parameters.
no code implementations • 2 Jun 2021 • Matthew Li, Laurent Demanet, Leonardo Zepeda-Núñez
We propose an end-to-end deep learning framework that comprehensively solves the inverse wave scattering problem across all length scales.
no code implementations • 24 Nov 2020 • Matthew Li, Laurent Demanet, Leonardo Zepeda-Núñez
We introduce an end-to-end deep learning architecture called the wide-band butterfly network (WideBNet) for approximating the inverse scattering map from wide-band scattering data.
no code implementations • 15 Nov 2020 • Mehak Aggarwal, Nishanth Arun, Sharut Gupta, Ashwin Vaswani, Bryan Chen, Matthew Li, Ken Chang, Jay Patel, Katherine Hoebel, Mishka Gidwani, Jayashree Kalpathy-Cramer, Praveer Singh
While success of Deep Learning (DL) in automated diagnosis can be transformative to the medicinal practice especially for people with little or no access to doctors, its widespread acceptability is severely limited by inherent black-box decision making and unsafe failure modes.