15 citations · 21 across the 8 of their papers we have counts for
8 papers
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…
Learning Actions from Human Demonstration Video for Robotic Manipulation
Shuo Yang, Wei Zhang, Weizhi Lu +2
Learning actions from human demonstration is an emerging trend for designing intelligent robotic systems, which can be referred as video to command. The performance of such approac…
Bipartite Graph based Construction of Compressed Sensing Matrices
Weizhi Lu, Kidiyo Kpalma, Joseph Ronsin
This paper proposes an efficient method to construct the bipartite graph with as many edges as possible while without introducing the shortest cycles of length equal to 4. The bina…
Sparse Matrix-based Random Projection for Classification
Weizhi Lu, Weiyu Li, Kidiyo Kpalma +1
As a typical dimensionality reduction technique, random projection can be simply implemented with linear projection, while maintaining the pairwise distances of high-dimensional da…
Compressed Sensing Matrices: Binary vs. Ternary
Weizhi Lu, Kidiyo Kpalma, Joseph Ronsin
Binary matrix and ternary matrix are two types of popular sensing matrices in compressed sensing for their competitive performance and low computation. However, to the best of our…
Near-optimal Binary Compressed Sensing Matrix
Weizhi Lu, Weiyu Li, Kidiyo Kpalma +1
Compressed sensing is a promising technique that attempts to faithfully recover sparse signal with as few linear and nonadaptive measurements as possible. Its performance is largel…