5 citations · 10 across the 4 of their papers we have counts for
4 papers
TA-MoE: Topology-Aware Large Scale Mixture-of-Expert Training
Chang Chen, Min Li, Zhihua Wu +2
Sparsely gated Mixture-of-Expert (MoE) has demonstrated its effectiveness in scaling up deep neural networks to an extreme scale. Despite that numerous efforts have been made to im…
a-Tucker: Input-Adaptive and Matricization-Free Tucker Decomposition for Dense Tensors on CPUs and GPUs
Min Li, Chuanfu Xiao, Chao Yang
Tucker decomposition is one of the most popular models for analyzing and compressing large-scale tensorial data. Existing Tucker decomposition algorithms usually rely on a single s…
Adaptive SpMV/SpMSpV on GPUs for Input Vectors of Varied Sparsity
Min Li, Yulong Ao, Chao Yang
Despite numerous efforts for optimizing the performance of Sparse Matrix and Vector Multiplication (SpMV) on modern hardware architectures, few works are done to its sparse counter…
Efficient Alternating Least Squares Algorithms for Low Multilinear Rank Approximation of Tensors
Chuanfu Xiao, Chao Yang, Min Li
The low multilinear rank approximation, also known as the truncated Tucker decomposition, has been extensively utilized in many applications that involve higher-order tensors. Popu…