Speaker
Sihao CHENG
(Johns Hopkins University)
Description
Textures and patterns are ubiquitous in imaging data but
challenging for quantitative analysis. I will present a new tool, called
the “scattering transform”. It borrows ideas from convolutional neural
nets while sharing advantages of traditional statistical
estimators. As an example, I will show its application to cosmic density
maps for cosmological parameter inference and show that it
outperforms classic statistics. It is a powerful new approach in
astrophysics and beyond.
Primary author
Sihao CHENG
(Johns Hopkins University)
Co-authors
Prof.
Brice MÉNARD
(Johns Hopkins Univerisity)
Dr
Yuan-Sen TING
(IAS)
Prof.
Joan BRUNA
(New York University )
