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pro vyhledávání: '"Tan, Hong Ye"'
The manifold hypothesis says that natural high-dimensional data is actually supported on or around a low-dimensional manifold. Recent success of statistical and learning-based methods empirically supports this hypothesis, due to outperforming classic
Externí odkaz:
http://arxiv.org/abs/2408.06996
Autor:
Tan, Hong Ye, Cai, Ziruo, Pereyra, Marcelo, Mukherjee, Subhadip, Tang, Junqi, Schönlieb, Carola-Bibiane
Imaging is a standard example of an inverse problem, where the task of reconstructing a ground truth from a noisy measurement is ill-posed. Recent state-of-the-art approaches for imaging use deep learning, spearheaded by unrolled and end-to-end model
Externí odkaz:
http://arxiv.org/abs/2404.05445
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 operators even when paired high-quality training data is scarcely available
Externí odkaz:
http://arxiv.org/abs/2311.08972
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 a deterministic evolution for particles rather than a stochastic diff
Externí odkaz:
http://arxiv.org/abs/2308.14945
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 called learned mirror descent (LMD), based on the classical mirror descen
Externí odkaz:
http://arxiv.org/abs/2308.05045
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 ISTA or ADMM, with applications in inverse problems and imaging. Provab
Externí odkaz:
http://arxiv.org/abs/2303.07271
Autor:
Tan, Hong Ye, Mukherjee, Subhadip, Tang, Junqi, Hauptmann, Andreas, Schönlieb, Carola-Bibiane
Publikováno v:
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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 mirror descent algorithm, where the mirror map is modelled using input-c
Externí odkaz:
http://arxiv.org/abs/2210.12238
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 been shown to outperform classical optimization algorithms in terms of c
Externí odkaz:
http://arxiv.org/abs/2206.06733
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