这系列产品文章最开始的

Learning fast approximations of sparse coding

K Gregor ， Y Lecun icml2010

针对dictionary有相关性的压缩感知的难题dl能带来的benefit

Bo Xin, Yizhou Wang, Wen Gao, Baoyuan Wang, and David Wipf, "Maximal Sparsity with Deep Networks?," Advances in Neural Information Processing Systems (NIPS), 2016

Hao He, Bo Xin, Satoshi Ikehata, and David Wipf, "From Bayesian Sparsity to Gated Recurrent Nets," Advances in Neural Information Processing Systems (NIPS), 2017.

在图象处理的运用

Deep ADMM-Net for compressive sensing MRI（nips16？，pami版本号改了frank Wolfe干了一个net）

收敛性的确保

Xiaohan Chen*, Jialin Liu*, Zhangyang Wang, Wotao Yin. “Theoretical Linear Convergence of Unfolded ISTA and its Practical Weights and Thresholds.” Advances in Neural Information Processing Systems (NIPS), 2018

arXiv:1808.05331 [pdf, other] cs.CV

On the Convergence of Learning-based Iterative Methods for Nonconvex Inverse Problems

Risheng Liu, Shichao Cheng, Yi He, Xin Fan, Zhouchen Lin, Zhongxuan Luo

在assumption很强【非常简单的每日任务】上 learning沒有必需 手动式设计方案weight能够 optimal

Jialin Liu*, Xiaohan Chen*, Zhangyang Wang, Wotao Yin. “ALISTA: Analytic Weights Are As Good As Learned Weights in LISTA.” In Proceedings of International Conference on Learning Representations (ICLR), 2019

包含假如考虑到下weijie su教师的

su，boyd，candes A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights

这一和近期火的neural ode也是一样的构思

Yiping Lu, Aoxiao Zhong, Quanzheng Li, Bin Dong. "Beyond Finite Layer Neural Network:Bridging Deep Architects and Numerical Differential Equations" Thirty-fifth International Conference on Machine Learning (ICML), 2018

操纵 提升 dl应当联络很深

用操纵角度观察提升⬇️

L. Lessard, B. Recht, and A. Packard. Analysis and design of optimization algorithms via integral quadratic constraints. SIAM Journal on Optimization, 26(1):57–95, 2016.

便是提升是最特别的梯度方向流，dl和操纵里许多现象都有可能并不是梯度方向流