1 citations · 1 across the 8 of their papers we have counts for
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Solving Imaging Inverse Problems Using Plug-and-Play Denoisers: Regularization and Optimization Perspectives
Hong Ye Tan, Subhadip Mukherjee, Junqi Tang
Inverse problems lie at the heart of modern imaging science, with broad applications in areas such as medical imaging, remote sensing, and microscopy. Recent years have witnessed a…
Boosting Data-Driven Mirror Descent with Randomization, Equivariance, and Acceleration
Hong Ye Tan, Subhadip Mukherjee, Junqi Tang +1
Learning-to-optimize (L2O) is an emerging research area in large-scale optimization with applications in data science. Recently, researchers have proposed a novel L2O framework cal…
Provably Convergent Plug-and-Play Quasi-Newton Methods
Hong Ye Tan, Subhadip Mukherjee, Junqi Tang +1
Plug-and-Play (PnP) methods are a class of efficient iterative methods that aim to combine data fidelity terms and deep denoisers using classical optimization algorithms, such as I…
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…
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…