26 citations · 137 across the 17 of their papers we have counts for
7 papers · 1 filter
CNN-Cap: Effective Convolutional Neural Network Based Capacitance Models for Full-Chip Parasitic Extraction
Dingcheng Yang, Wenjian Yu, Yuanbo Guo +1
Accurate capacitance extraction is becoming more important for designing integrated circuits under advanced process technology. The pattern matching based full-chip extraction meth…
Efficient Model-Based Collaborative Filtering with Fast Adaptive PCA
Xiangyun Ding, Wenjian Yu, Yuyang Xie +1
A model-based collaborative filtering (CF) approach utilizing fast adaptive randomized singular value decomposition (SVD) is proposed for the matrix completion problem in recommend…
DP-Net: Dynamic Programming Guided Deep Neural Network Compression
Dingcheng Yang, Wenjian Yu, Ao Zhou +3
In this work, we propose an effective scheme (called DP-Net) for compressing the deep neural networks (DNNs). It includes a novel dynamic programming (DP) based algorithm to obtain…
Kernelized Support Tensor Train Machines
Cong Chen, Kim Batselier, Wenjian Yu +1
Tensor, a multi-dimensional data structure, has been exploited recently in the machine learning community. Traditional machine learning approaches are vector- or matrix-based, and…
Faster Matrix Completion Using Randomized SVD
Xu Feng, Wenjian Yu, Yaohang Li
Matrix completion is a widely used technique for image inpainting and personalized recommender system, etc. In this work, we focus on accelerating the matrix completion using faste…
Fast Randomized PCA for Sparse Data
Xu Feng, Yuyang Xie, Mingye Song +2
Principal component analysis (PCA) is widely used for dimension reduction and embedding of real data in social network analysis, information retrieval, and natural language process…
A Unified Approximation Framework for Compressing and Accelerating Deep Neural Networks
Yuzhe Ma, Ran Chen, Wei Li +4
Deep neural networks (DNNs) have achieved significant success in a variety of real world applications, i.e., image classification. However, tons of parameters in the networks restr…