121 citations · 141 across the 6 of their papers we have counts for
6 papers
Matrix Factorization on GPUs with Memory Optimization and Approximate Computing
Wei Tan, Shiyu Chang, Liana Fong +3
Matrix factorization (MF) discovers latent features from observations, which has shown great promises in the fields of collaborative filtering, data compression, feature extraction…
CuLDA_CGS: Solving Large-scale LDA Problems on GPUs
Xiaolong Xie, Yun Liang, Xiuhong Li +1
Latent Dirichlet Allocation(LDA) is a popular topic model. Given the fact that the input corpus of LDA algorithms consists of millions to billions of tokens, the LDA training proce…
An influence-based fast preceding questionnaire model for elderly assessments
Tong Mo, Rong Zhang, Weiping Li +3
To improve the efficiency of elderly assessments, an influence-based fast preceding questionnaire model (FPQM) is proposed. Compared with traditional assessments, the FPQM optimize…
Dilated Recurrent Neural Networks
Shiyu Chang, Yang Zhang, Wei Han +7
Learning with recurrent neural networks (RNNs) on long sequences is a notoriously difficult task. There are three major challenges: 1) complex dependencies, 2) vanishing and explod…
CuMF_SGD: Fast and Scalable Matrix Factorization
Xiaolong Xie, Wei Tan, Liana L. Fong +1
Matrix factorization (MF) has been widely used in e.g., recommender systems, topic modeling and word embedding. Stochastic gradient descent (SGD) is popular in solving MF problems…
Faster and Cheaper: Parallelizing Large-Scale Matrix Factorization on GPUs
Wei Tan, Liangliang Cao, Liana Fong
Matrix factorization (MF) is employed by many popular algorithms, e.g., collaborative filtering. The emerging GPU technology, with massively multicore and high intra-chip memory ba…