research · Rosenkreutz Studio

A review on the paper "On the convergence of ADAM and beyond".

A review on the paper "ADAM: A Method for Stochastic Optimization".

A deduction for the basic form of the training phase and testing phases in sparse coding and dictionary learning.

Comments on two SEG-2018 expanded abstracts. In this article, the theories of those two papers would be doubted.

Weekly notes. In this note we are mainly learning "Solving ill-posed inverse problems using iterative deep neural networks".

Weekly notes. In this note we are mainly learning "NETT: Solving Inverse Problems with Deep Neural Networks".

The note for weekly report on Feb 23, 2018.

A collection for other's researches about inverse problem. This article is an introduction and also a categorization.

A collection of the discussions about the deep-learning implementation of proximal learning, and conventional inverse algorithms like ISTA and AMP.

A collection for abstracts of those papers related to inverse problem enhanced by deep learning. This is the part I.

The note is just for one important article, "Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization", Part I.

The note for weekly report on May 26, 2018.

The note for weekly report on May 26, 2018.

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