8 citations · 8 across the 7 of their papers we have counts for
10 papers
Simultaneous Best Subset Selection and Dimension Reduction via Primal-Dual Iterations
Canhong Wen, Ruipeng Dong, Xueqin Wang +2
Sparse reduced rank regression is an essential statistical learning method. In the contemporary literature, estimation is typically formulated as a nonconvex optimization that ofte…
Ternary and Binary Quantization for Improved Classification
Weizhi Lu, Mingrui Chen, Kai Guo +1
Dimension reduction and data quantization are two important methods for reducing data complexity. In the paper, we study the methodology of first reducing data dimension by random…
Cascaded Compressed Sensing Networks: A Reversible Architecture for Layerwise Learning
Weizhi Lu, Mingrui Chen, Kai Guo +1
Recently, the method that learns networks layer by layer has attracted increasing interest for its ease of analysis. For the method, the main challenge lies in deriving an optimiza…
Deep Learning to Ternary Hash Codes by Continuation
Mingrui Chen, Weiyu Li, Weizhi Lu
Recently, it has been observed that {0,1,-1}-ternary codes which are simply generated from deep features by hard thresholding, tend to outperform {-1,1}-binary codes in image retri…
Online Updating Statistics for Heterogenous Updating Regressions via Homogenization Techniques
Lin Lu, Lu Jun, Li Weiyu
Under the environment of big data streams, it is a common situation where the variable set of a model may change according to the condition of data streams. In this paper, we propo…
Stochastic Alternating Direction Method of Multipliers for Byzantine-Robust Distributed Learning
Feng Lin, Weiyu Li, Qing Ling
This paper aims to solve a distributed learning problem under Byzantine attacks. In the underlying distributed system, a number of unknown but malicious workers (termed as Byzantin…