9 citations · 15 across the 5 of their papers we have counts for
7 papers
Temporal Network Representation Learning via Historical Neighborhoods Aggregation
Shixun Huang, Zhifeng Bao, Guoliang Li +2
Network embedding is an effective method to learn low-dimensional representations of nodes, which can be applied to various real-life applications such as visualization, node class…
Towards Interpretable and Learnable Risk Analysis for Entity Resolution
Zhaoqiang Chen, Qun Chen, Boyi Hou +3
Machine-learning-based entity resolution has been widely studied. However, some entity pairs may be mislabeled by machine learning models and existing studies do not study the risk…
An End-to-End Deep RL Framework for Task Arrangement in Crowdsourcing Platforms
Caihua Shan, Nikos Mamoulis, Reynold Cheng +3
In this paper, we propose a Deep Reinforcement Learning (RL) framework for task arrangement, which is a critical problem for the success of crowdsourcing platforms. Previous works…
An End-to-End Learning-based Cost Estimator
Ji Sun, Guoliang Li
Cost and cardinality estimation is vital to query optimizer, which can guide the plan selection. However traditional empirical cost and cardinality estimation techniques cannot pro…
VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository
Kevin Hu, Neil Gaikwad, Michiel Bakker +7
Researchers currently rely on ad hoc datasets to train automated visualization tools and evaluate the effectiveness of visualization designs. These exemplars often lack the charact…
Trajectory-driven Influential Billboard Placement
Ping Zhang, Zhifeng Bao, Yuchen Li +3
In this paper we propose and study the problem of trajectory-driven influential billboard placement: given a set of billboards (each with a location and a cost), a database of…