activity
20182021
most citedDEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification

15 citations · 31 across the 7 of their papers we have counts for

collaborators

11 papers

cs.IR20211 cited

Controllable Gradient Item Retrieval

Haonan Wang, Chang Zhou, Carl Yang +2

In this paper, we identify and study an important problem of gradient item retrieval. We define the problem as retrieving a sequence of items with a gradual change on a certain att…

cs.HC20202 cited

A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning Processes

Yuxin Ma, Arlen Fan, Jingrui He +2

Many statistical learning models hold an assumption that the training data and the future unlabeled data are drawn from the same distribution. However, this assumption is difficult…

cs.LG20203 cited

Generic Outlier Detection in Multi-Armed Bandit

Yikun Ban, Jingrui He

In this paper, we study the problem of outlier arm detection in multi-armed bandit settings, which finds plenty of applications in many high-impact domains such as finance, healthc…

cs.LG20206 cited

Continuous Transfer Learning with Label-informed Distribution Alignment

Jun Wu, Jingrui He

Transfer learning has been successfully applied across many high-impact applications. However, most existing work focuses on the static transfer learning setting, and very little i…

cs.DC2019

Coalesced TLB to Exploit Diverse Contiguity of Memory Mapping

Yikun Ban, Yuchen Zhou, Xu Cheng +1

The miss rate of TLB is crucial to the performance of address translation for virtual memory. To reduce the TLB misses, improving translation coverage of TLB has been an primary ap…

cs.LG201915 cited

DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification

Jun Wu, Jingrui He, Jiejun Xu

Graph data widely exist in many high-impact applications. Inspired by the success of deep learning in grid-structured data, graph neural network models have been proposed to learn…