402 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Alibaba Group (China)CN60 papers
- Zhejiang UniversityCN46 papers
- Bellevue Hospital CenterUS30 papers
- Peking UniversityCN22 papers
- Nanyang Technological UniversitySG21 papers
- Tsinghua UniversityCN15 papers
- Chinese Academy of SciencesCN14 papers
- Renmin University of ChinaCN13 papers
- Shanghai Jiao Tong UniversityCN11 papers
- Tianjin UniversityCN11 papers
- Alibaba Group (Cayman Islands)KY10 papers
- East China Normal UniversityCN10 papers
12 papers · 1 filter
SwinRDM: Integrate SwinRNN with Diffusion Model towards High-Resolution and High-Quality Weather Forecasting
Lei Chen, Fei Du, Yuan Hu +2
Data-driven medium-range weather forecasting has attracted much attention in recent years. However, the forecasting accuracy at high resolution is unsatisfactory currently. Pursuin…
Billion-scale Pre-trained E-commerce Product Knowledge Graph Model
Wen Zhang, Chi-Man Wong, Ganqiang Ye +3
In recent years, knowledge graphs have been widely applied to organize data in a uniform way and enhance many tasks that require knowledge, for example, online shopping which has g…
Dual-View Distilled BERT for Sentence Embedding
Xingyi Cheng
Recently, BERT realized significant progress for sentence matching via word-level cross sentence attention. However, the performance significantly drops when using siamese BERT-net…
Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph Completion
Guanglin Niu, Yang Li, Chengguang Tang +7
Aiming at expanding few-shot relations' coverage in knowledge graphs (KGs), few-shot knowledge graph completion (FKGC) has recently gained more research interests. Some existing mo…
Metapaths guided Neighbors aggregated Network for?Heterogeneous Graph Reasoning
Bang Lin, Xiuchong Wang, Yu Dong +3
Most real-world datasets are inherently heterogeneous graphs, which involve a diversity of node and relation types. Heterogeneous graph embedding is to learn the structure and sema…
Price Suggestion for Online Second-hand Items with Texts and Images
Liang Han, Zhaozheng Yin, Zhurong Xia +2
This paper presents an intelligent price suggestion system for online second-hand listings based on their uploaded images and text descriptions. The goal of price prediction is to…
FSPN: A New Class of Probabilistic Graphical Model
Ziniu Wu, Rong Zhu, Andreas Pfadler +5
We introduce factorize sum split product networks (FSPNs), a new class of probabilistic graphical models (PGMs). FSPNs are designed to overcome the drawbacks of existing PGMs in te…
DualDE: Dually Distilling Knowledge Graph Embedding for Faster and Cheaper Reasoning
Yushan Zhu, Wen Zhang, Mingyang Chen +4
Knowledge Graph Embedding (KGE) is a popular method for KG reasoning and training KGEs with higher dimension are usually preferred since they have better reasoning capability. Howe…
Spending Money Wisely: Online Electronic Coupon Allocation based on Real-Time User Intent Detection
Liangwei Li, Liucheng Sun, Chenwei Weng +2
Online electronic coupon (e-coupon) is becoming a primary tool for e-commerce platforms to attract users to place orders. E-coupons are the digital equivalent of traditional paper…
Temporal-adaptive Hierarchical Reinforcement Learning
Wen-Ji Zhou, Yang Yu
Hierarchical reinforcement learning (HRL) helps address large-scale and sparse reward issues in reinforcement learning. In HRL, the policy model has an inner representation structu…
Can Sophisticated Dispatching Strategy Acquired by Reinforcement Learning? - A Case Study in Dynamic Courier Dispatching System
Yujie Chen, Yu Qian, Yichen Yao +5
In this paper, we study a courier dispatching problem (CDP) raised from an online pickup-service platform of Alibaba. The CDP aims to assign a set of couriers to serve pickup reque…
ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation
Chang Zhou, Jinze Bai, Junshuai Song +4
A user can be represented as what he/she does along the history. A common way to deal with the user modeling problem is to manually extract all kinds of aggregated features over th…