1 citations · 1 across the 8 of their papers we have counts for
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Unsupervised approaches based on optimal transport and convex analysis for inverse problems in imaging
Marcello Carioni, Subhadip Mukherjee, Hong Ye Tan +1
Unsupervised deep learning approaches have recently become one of the crucial research areas in imaging owing to their ability to learn expressive and powerful reconstruction opera…
Noise-Free Sampling Algorithms via Regularized Wasserstein Proximals
Hong Ye Tan, Stanley Osher, Wuchen Li
We consider the problem of sampling from a distribution governed by a potential function. This work proposes an explicit score based MCMC method that is deterministic, resulting in…
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…