1 citations · 1 across the 3 of their papers we have counts for
5 papers
Path-based Deep Network for Candidate Item Matching in Recommenders
Houyi Li, Zhihong Chen, Chenliang Li +5
The large-scale recommender system mainly consists of two stages: matching and ranking. The matching stage (also known as the retrieval step) identifies a small fraction of relevan…
GIPA: General Information Propagation Algorithm for Graph Learning
Qinkai Zheng, Houyi Li, Peng Zhang +4
Graph neural networks (GNNs) have been popularly used in analyzing graph-structured data, showing promising results in various applications such as node classification, link predic…
Woodpecker-DL: Accelerating Deep Neural Networks via Hardware-Aware Multifaceted Optimizations
Yongchao Liu, Yue Jin, Yong Chen +4
Accelerating deep model training and inference is crucial in practice. Existing deep learning frameworks usually concentrate on optimizing training speed and pay fewer attentions t…
LightScan: Faster Scan Primitive on CUDA Compatible Manycore Processors
Yongchao Liu, Srinivas Aluru
Scan (or prefix sum) is a fundamental and widely used primitive in parallel computing. In this paper, we present LightScan, a faster parallel scan primitive for CUDA-enabled GPUs,…
High-speed and accurate color-space short-read alignment with CUSHAW2
Yongchao Liu, Bernt Popp, Bertil Schmidt
Summary: We present an extension of CUSHAW2 for fast and accurate alignments of SOLiD color-space short-reads. Our extension introduces a double-seeding approach to improve mapping…