26 citations · 137 across the 17 of their papers we have counts for
17 papers
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 Tensor Train Decomposition for Sparse Data
Lingjie Li, Wenjian Yu, Kim Batselier
In recent years, the application of tensors has become more widespread in fields that involve data analytics and numerical computation. Due to the explosive growth of data, low-ran…
Optimal Algorithm for Profiling Dynamic Arrays with Finite Values
Dingcheng Yang, Wenjian Yu, Junhui Deng +1
How can one quickly answer the most and top popular objects at any time, given a large log stream in a system of billions of users? It is equivalent to find the mode and top-freque…
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…
Fast and Accurate Tensor Completion with Total Variation Regularized Tensor Trains
Ching-Yun Ko, Kim Batselier, Wenjian Yu +1
We propose a new tensor completion method based on tensor trains. The to-be-completed tensor is modeled as a low-rank tensor train, where we use the known tensor entries and their…
Computing low-rank approximations of large-scale matrices with the Tensor Network randomized SVD
Kim Batselier, Wenjian Yu, Luca Daniel +1
We propose a new algorithm for the computation of a singular value decomposition (SVD) low-rank approximation of a matrix in the Matrix Product Operator (MPO) format, also called t…
Single-Pass PCA of Large High-Dimensional Data
Wenjian Yu, Yu Gu, Jian Li +2
Principal component analysis (PCA) is a fundamental dimension reduction tool in statistics and machine learning. For large and high-dimensional data, computing the PCA (i.e., the s…
A Fast Implementation of Singular Value Thresholding Algorithm using Recycling Rank Revealing Randomized Singular Value Decomposition
Yaohang Li, Wenjian Yu
In this paper, we present a fast implementation of the Singular Value Thresholding (SVT) algorithm for matrix completion. A rank-revealing randomized singular value decomposition (…
Efficient Randomized Algorithms for the Fixed-Precision Low-Rank Matrix Approximation
Wenjian Yu, Yu Gu, Yaohang Li
Randomized algorithms for low-rank matrix approximation are investigated, with the emphasis on the fixed-precision problem and computational efficiency for handling large matrices.…
A Rank Revealing Randomized Singular Value Decomposition (R3SVD) Algorithm for Low-rank Matrix Approximations
Hao Ji, Wenjian Yu, Yaohang Li
In this paper, we present a Rank Revealing Randomized Singular Value Decomposition (R3SVD) algorithm to incrementally construct a low-rank approximation of a potentially large matr…
An Algorithmic Framework for Efficient Large-Scale Circuit Simulation Using Exponential Integrators
Hao Zhuang, Wenjian Yu, Ilgweon Kang +2
We propose an efficient algorithmic framework for time domain circuit simulation using exponential integrator. This work addresses several critical issues exposed by previous matri…
Simulation Algorithms with Exponential Integration for Time-Domain Analysis of Large-Scale Power Delivery Networks
Hao Zhuang, Wenjian Yu, Shih-Hung Weng +5
We design an algorithmic framework using matrix exponentials for time-domain simulation of power delivery network (PDN). Our framework can reuse factorized matrices to simulate the…