12 citations · 12 across the 6 of their papers we have counts for
6 papers
cuFastTuckerPlus: A Stochastic Parallel Sparse FastTucker Decomposition Using GPU Tensor Cores
Zixuan Li, Mingxing Duan, Huizhang Luo +3
Sparse tensors are prevalent in real-world applications, often characterized by their large-scale, high-order, and high-dimensional nature. Directly handling raw tensors is impract…
cuFasterTucker: A Stochastic Optimization Strategy for Parallel Sparse FastTucker Decomposition on GPU Platform
Zixuan Li
Currently, the size of scientific data is growing at an unprecedented rate. Data in the form of tensors exhibit high-order, high-dimensional, and highly sparse features. Although t…
cu_FastTucker: A Faster and Stabler Stochastic Optimization for Parallel Sparse Tucker Decomposition on Multi-GPUs
Zixuan Li
High-Order, High-Dimension, and Sparse Tensor (HOHDST) data originates from real industrial applications, i.e., social networks, recommender systems, bio-information, and traffic i…
Locality Sensitive Hash Aggregated Nonlinear Neighbourhood Matrix Factorization for Online Sparse Big Data Analysis
Zixuan Li, Hao Li, Kenli Li +3
Matrix factorization (MF) can extract the low-rank features and integrate the information of the data manifold distribution from high-dimensional data, which can consider the nonli…
Comparative Synthesis: Learning Near-Optimal Network Designs by Query
Yanjun Wang, Zixuan Li, Chuan Jiang +2
When managing wide-area networks, network architects must decide how to balance multiple conflicting metrics, and ensure fair allocations to competing traffic while prioritizing cr…
SGD_Tucker: A Novel Stochastic Optimization Strategy for Parallel Sparse Tucker Decomposition
Hao Li, Zixuan Li, Kenli Li +3
Sparse Tucker Decomposition (STD) algorithms learn a core tensor and a group of factor matrices to obtain an optimal low-rank representation feature for the \underline{H}igh-\under…