3.7k citations · 4.1k across the 25 of their papers we have counts for
6 papers · 1 filter
Optimization Induced Equilibrium Networks
Xingyu Xie, Qiuhao Wang, Zenan Ling +4
Implicit equilibrium models, i.e., deep neural networks (DNNs) defined by implicit equations, have been becoming more and more attractive recently. In this paper, we investigate an…
Time Series Forecasting via Learning Convolutionally Low-Rank Models
Guangcan Liu
Recently, Liu and Zhang studied the rather challenging problem of time series forecasting from the perspective of compressed sensing. They proposed a no-learning method, named Conv…
Maximum-and-Concatenation Networks
Xingyu Xie, Hao Kong, Jianlong Wu +3
While successful in many fields, deep neural networks (DNNs) still suffer from some open problems such as bad local minima and unsatisfactory generalization performance. In this wo…
Learning Hybrid Representation by Robust Dictionary Learning in Factorized Compressed Space
Jiahuan Ren, Zhao Zhang, Sheng Li +4
In this paper, we investigate the robust dictionary learning (DL) to discover the hybrid salient low-rank and sparse representation in a factorized compressed space. A Joint Robust…
Recovery of Future Data via Convolution Nuclear Norm Minimization
Guangcan Liu, Wayne Zhang
This paper studies the problem of time series forecasting (TSF) from the perspective of compressed sensing. First of all, we convert TSF into a more inclusive problem called tensor…
Differentiable Linearized ADMM
Xingyu Xie, Jianlong Wu, Zhisheng Zhong +2
Recently, a number of learning-based optimization methods that combine data-driven architectures with the classical optimization algorithms have been proposed and explored, showing…