activity
20192021
most citedRevisiting Over-smoothing in Deep GCNs

56 citations · 87 across the 6 of their papers we have counts for

collaborators

6 papers

eess.SP2021★ 20 cited

Self-supervised EEG Representation Learning for Automatic Sleep Staging

Chaoqi Yang, Danica Xiao, M. Brandon Westover +1

Background: Deep learning models have shown great success in automating tasks in sleep medicine by learning from carefully annotated Electroencephalogram (EEG) data. However, effec…

physics.soc-ph2020

Analyzing the Design Space of Re-opening Policies and COVID-19 Outcomes in the US

Chaoqi Yang, Ruijie Wang, Fangwei Gao +3

Recent re-opening policies in the US, following a period of social distancing measures, introduced a significant increase in daily COVID-19 infections, calling for a roll-back or s…

cs.CY2020★ 4 cited

The Paradox of Information Access: Growing Isolation in the Age of Sharing

Tarek Abdelzaher, Heng Ji, Jinyang Li +5

Modern online media, such as Twitter, Instagram, and YouTube, enable anyone to become an information producer and to offer online content for potentially global consumption. By inc…

cs.LG2020★ 56 cited

Revisiting Over-smoothing in Deep GCNs

Chaoqi Yang, Ruijie Wang, Shuochao Yao +2

Oversmoothing has been assumed to be the major cause of performance drop in deep graph convolutional networks (GCNs). In this paper, we propose a new view that deep GCNs can actual…

cs.AI2020★ 5 cited

MoTiAC: Multi-Objective Actor-Critics for Real-Time Bidding

Haolin Zhou, Chaoqi Yang, Xiaofeng Gao +3

Online Real-Time Bidding (RTB) is a complex auction game among which advertisers struggle to bid for ad impressions when a user request occurs. Considering display cost, Return on…

cs.SI2019★ 2 cited

An Induced Multi-Relational Framework for Answer Selection in Community Question Answer Platforms

Kanika Narang, Chaoqi Yang, Adit Krishnan +3

This paper addresses the question of identifying the best candidate answer to a question on Community Question Answer (CQA) forums. The problem is important because Individuals oft…