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Convolutional Neural Networks (CNNs) are now a well-established tool for solving computational imaging problems.
Bayesian learning for neural networks
Radford M Neal · 1995
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Using the nyström method to speed up kernel machines
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A non-local algorithm for image denoising
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Image denoising by sparse 3-d transform-domain collaborative filtering
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Kernel regression for image processing and reconstruction
H. Takeda, S. Farsiu, and P. Milanfar · 2007
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S. Ramani, T. Blu, and M. Unser · 2008
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Kernel methods for deep learning
Youngmin Cho and Lawrence K Saul · 2009
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Sparse and redundant representations: from theory to applications in signal and image processing
Michael Elad · 2010
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A tour of modern image filtering: New insights and methods, both practical and theoretical
Peyman Milanfar · 2012
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Plug-and-play priors for model based reconstruction
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Understanding deep learning requires rethinking generalization
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Efficient algorithms for convolutional sparse representations
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Lukas Balles and Philipp Hennig · 2017
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K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang · 2017
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The marginal value of adaptive gradient methods in machine learning
Ashia C Wilson, Rebecca Roelofs, Mitchell Stern, Nati Srebro, and Benjamin Recht · 2017
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On exact computation with an infinitely wide neural net
Sanjeev Arora, Simon S Du, Wei Hu, Zhiyuan Li, Russ R Salakhutdinov, and Ruosong Wang · 2019
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Z. Cheng, M. Gadelha, S. Maji, and D. Sheldon · 2019
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Bayesian deep convolutional networks with many channels are gaussian processes
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Lechao Xiao, Yasaman Bahri, Jascha Sohl-Dickstein, Samuel S Schoenholz, and Jeffrey Pennington · 2018
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Wide neural networks of any depth evolve as linear models under gradient descent
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Rare: Image reconstruction using deep priors learned without ground truth
J. Liu, Y. Sun, C. Eldeniz, W. Gan, H. An, and U. S. Kamilov · 2020
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