Update README.md

README.md

@@ -1,6 +1,3 @@
 # How to do Physics-based Learning
 
-Author: Michael Kellman
-Date: 4/23/2020
-
-Computational imaging systems (\eg tomographic systems, computational optics, magnetic resonance imaging) jointly design software and hardware to retrieve information which is not traditionally accessible. Generally, such systems are characterized by how the information is encoded (forward process) and decoded (inverse problem) from the measurements. Critical aspects of computational imaging systems, such as experimental design and image priors, can be optimized through deep networks formed by \textit{unrolling} the iterations of classical model-based reconstructions~\cite{Gregor:2010, sun2016deep, kellman2019physics}.
+Computational imaging systems (\eg tomographic systems, computational optics, magnetic resonance imaging) jointly design software and hardware to retrieve information which is not traditionally accessible. Generally, such systems are characterized by how the information is encoded (forward process) and decoded (inverse problem) from the measurements. Critical aspects of computational imaging systems, such as experimental design and image priors, can be optimized through deep networks formed by \textit{unrolling} the iterations of classical model-based reconstructions.