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Bo Wen

The University of Hong Kong

Experimentally realized memristive memory augmented neural network



Ruibin Mao , Bo Wen , Yahui Zhao , Arman Kazemi , Ann Franchesca Laguna , Michael Neimier , X. Sharon Hu , Xia Sheng , Catherine E. Graves , John Paul Strachan , Can Li

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* 54 pages, 21 figures, 3 tables 
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BERT-ASC: Auxiliary-Sentence Construction for Implicit Aspect Learning in Sentiment Analysis



Ahmed Murtadha , Shengfeng Pan , Bo Wen , Jianlin Su , Wenze Zhang , Yunfeng Liu

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* under review 
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PKGM: A Pre-trained Knowledge Graph Model for E-commerce Application



Wen Zhang , Chi-Man Wong , Ganqinag Ye , Bo Wen , Hongting Zhou , Wei Zhang , Huajun Chen

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* This is an extension of work "Billion-scale Pre-trained E-commerce Product Knowledge Graph Model" published at ICDE2021. We test PKGM on two additional tasks, scene detection and sequential recommendation, and add serving with item embeddings as one of the baseline. The extensive experiments show the effectiveness of PKGM, pre-trained knowledge graph model. arXiv admin note: text overlap with arXiv:2105.00388 
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Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting



Ling Chen , Donghui Chen , Zongjiang Shang , Youdong Zhang , Bo Wen , Chenghu Yang

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Scale-Aware Neural Architecture Search for Multivariate Time Series Forecasting



Donghui Chen , Ling Chen , Zongjiang Shang , Youdong Zhang , Bo Wen , Chenghu Yang

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Learning from Multiple Time Series: A Deep Disentangled Approach to Diversified Time Series Forecasting



Ling Chen , Weiqi Chen , Binqing Wu , Youdong Zhang , Bo Wen , Chenghu Yang

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Billion-scale Pre-trained E-commerce Product Knowledge Graph Model



Wen Zhang , Chi-Man Wong , Ganqiang Ye , Bo Wen , Wei Zhang , Huajun Chen

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* Paper accepted by ICDE2021 
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RoFormer: Enhanced Transformer with Rotary Position Embedding



Jianlin Su , Yu Lu , Shengfeng Pan , Bo Wen , Yunfeng Liu

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* Preprint. English experiments are coming 
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Linear Convergence of Proximal Gradient Algorithm with Extrapolation for a Class of Nonconvex Nonsmooth Minimization Problems



Bo Wen , Xiaojun Chen , Ting Kei Pong

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* We have replaced the blanket assumptions on $f+g$ by the (weaker) assumptions that the optimal value of (1.1) is finite and attained. Section 3.4 has been deleted 
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