activity
20162021
most citedAdaRNN: Adaptive Learning and Forecasting of Time Series

37 citations · 74 across the 6 of their papers we have counts for

collaborators

7 papers

cs.IR202127 cited

Learning an Adaptive Meta Model-Generator for Incrementally Updating Recommender Systems

Danni Peng, Sinno Jialin Pan, Jie Zhang +1

Recommender Systems (RSs) in real-world applications often deal with billions of user interactions daily. To capture the most recent trends effectively, it is common to update the…

cs.LG202137 cited

AdaRNN: Adaptive Learning and Forecasting of Time Series

Yuntao Du, Jindong Wang, Wenjie Feng +4

Time series has wide applications in the real world and is known to be difficult to forecast. Since its statistical properties change over time, its distribution also changes tempo…

cs.LG2021

Mitigating Performance Saturation in Neural Marked Point Processes: Architectures and Loss Functions

Tianbo Li, Tianze Luo, Yiping Ke +1

Attributed event sequences are commonly encountered in practice. A recent research line focuses on incorporating neural networks with the statistical model -- marked point processe…

cs.LG20203 cited

Reinforcement Learning with Efficient Active Feature Acquisition

Haiyan Yin, Yingzhen Li, Sinno Jialin Pan +2

Solving real-life sequential decision making problems under partial observability involves an exploration-exploitation problem. To be successful, an agent needs to efficiently gath…

cs.CL20197 cited

Integrating Deep Learning with Logic Fusion for Information Extraction

Wenya Wang, Sinno Jialin Pan

Information extraction (IE) aims to produce structured information from an input text, e.g., Named Entity Recognition and Relation Extraction. Various attempts have been proposed f…

cs.LG2019

Transfer Value Iteration Networks

Junyi Shen, Hankz Hankui Zhuo, Jin Xu +2

Value iteration networks (VINs) have been demonstrated to have a good generalization ability for reinforcement learning tasks across similar domains. However, based on our experime…