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20082023
most citedBootstrap Latent Representations for Multi-modal Recommendation

290 citations · 1.4k across the 86 of their papers we have counts for

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Showing 2019Show all

12 papers · 1 filter

cs.CL2019★ 10 cited

Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations

Peixiang Zhong, Di Wang, Chunyan Miao

Messages in human conversations inherently convey emotions. The task of detecting emotions in textual conversations leads to a wide range of applications such as opinion mining in…

cs.CL2019★ 2 cited

Commonsense Knowledge + BERT for Level 2 Reading Comprehension Ability Test

Yidan Hu, Gongqi Lin, Yuan Miao +1

Commonsense knowledge plays an important role when we read. The performance of BERT on SQuAD dataset shows that the accuracy of BERT can be better than human users. However, it doe…

cs.CL2019

Reading Comprehension Ability Test-A Turing Test for Reading Comprehension

Yuan Miao, Gongqi Lin, Yidan Hu +1

Reading comprehension is an important ability of human intelligence. Literacy and numeracy are two most essential foundation for people to succeed at study, at work and in life. Re…

cs.NI2019★ 146 cited

Federated Learning in Mobile Edge Networks: A Comprehensive Survey

Wei Yang Bryan Lim, Nguyen Cong Luong, Dinh Thai Hoang +5

In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless…

cs.MM2019★ 7 cited

Generating Persuasive Visual Storylines for Promotional Videos

Chang Liu, Yi Dong, Han Yu +8

Video contents have become a critical tool for promoting products in E-commerce. However, the lack of automatic promotional video generation solutions makes large-scale video-based…

cs.CV2019★ 46 cited

EEG-Based Emotion Recognition Using Regularized Graph Neural Networks

Peixiang Zhong, Di Wang, Chunyan Miao

Electroencephalography (EEG) measures the neuronal activities in different brain regions via electrodes. Many existing studies on EEG-based emotion recognition do not fully exploit…