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Krylov subspace, which is generated by multiplying a given vector by the matrix of a linear transformation and its successive powers, has been extensively studied in classical optimization literature to design algorithms that converge quickly for large linear inverse problems.
Nonuniform fast fourier transforms using min-max interpolation
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Jacobian-free newton–krylov methods: a survey of approaches and applications
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Tweedie’s formula and selection bias
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A connection between score matching and denoising autoencoders
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Krylov subspace methods: principles and analysis
Jörg Liesen and Zdenek Strakos · 2013
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Proximal algorithms
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Martin Uecker, Peng Lai, Mark J Murphy, Patrick Virtue, Michael Elad, John M Pauly, Shreyas S Vasanawala, and Michael Lustig · 2014
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An efficient statistical method for image noise level estimation
Guangyong Chen, Fengyuan Zhu, and Pheng Ann Heng · 2015
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Deep convolutional neural network for inverse problems in imaging
Kyong Hwan Jin, Michael T McCann, Emmanuel Froustey, and Michael Unser · 2017
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Beyond a gaussian denoiser: Residual learning of deep CNN for image denoising
Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang · 2017
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fastMRI: An open dataset and benchmarks for accelerated MRI
Jure Zbontar, Florian Knoll, Anuroop Sriram, Tullie Murrell, Zhengnan Huang, Matthew J Muckley, Aaron Defazio, Ruben Stern, Patricia Johnson, Mary Bruno, et al · 2018
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Yang Song and Stefano Ermon · 2019
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Soft truncation: A universal training technique of score-based diffusion model for high precision score estimation
Dongjun Kim, Seungjae Shin, Kyungwoo Song, Wanmo Kang, and Il-Chul Moon · 2022
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Anish Lahiri, Marc Klasky, Jeffrey A Fessler, and Saiprasad Ravishankar · 2022
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Dreamfusion: Text-to-3d using 2d diffusion
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