16 citations · 74 across the 31 of their papers we have counts for
5 papers · 1 filter
Robust Data-Driven Accelerated Mirror Descent
Hong Ye Tan, Subhadip Mukherjee, Junqi Tang +2
Learning-to-optimize is an emerging framework that leverages training data to speed up the solution of certain optimization problems. One such approach is based on the classical mi…
Practical Operator Sketching Framework for Accelerating Iterative Data-Driven Solutions in Inverse Problems
Junqi Tang, Guixian Xu, Subhadip Mukherjee +1
We propose a new operator-sketching paradigm for designing efficient iterative data-driven reconstruction (IDR) schemes, e.g. Plug-and-Play algorithms and deep unrolling networks.…
Tree species classification from hyperspectral data using graph-regularized neural networks
Debmita Bandyopadhyay, Subhadip Mukherjee, James Ball +3
We propose a novel graph-regularized neural network (GRNN) algorithm for tree species classification. The proposed algorithm encompasses superpixel-based segmentation for graph con…
Data-Driven Mirror Descent with Input-Convex Neural Networks
Hong Ye Tan, Subhadip Mukherjee, Junqi Tang +1
Learning-to-optimize is an emerging framework that seeks to speed up the solution of certain optimization problems by leveraging training data. Learned optimization solvers have be…
Learned reconstruction methods with convergence guarantees
Subhadip Mukherjee, Andreas Hauptmann, Ozan Öktem +2
In recent years, deep learning has achieved remarkable empirical success for image reconstruction. This has catalyzed an ongoing quest for precise characterization of correctness a…