129 citations · 217 across the 9 of their papers we have counts for
9 papers
MV-HAN: A Hybrid Attentive Networks based Multi-View Learning Model for Large-scale Contents Recommendation
Ge Fan, Chaoyun Zhang, Kai Wang +1
Industrial recommender systems usually employ multi-source data to improve the recommendation quality, while effectively sharing information between different data sources remain a…
Deep Neural Mobile Networking
Chaoyun Zhang
The next generation of mobile networks is set to become increasingly complex, as these struggle to accommodate tremendous data traffic demands generated by ever-more connected devi…
CloudLSTM: A Recurrent Neural Model for Spatiotemporal Point-cloud Stream Forecasting
Chaoyun Zhang, Marco Fiore, Iain Murray +1
This paper introduces CloudLSTM, a new branch of recurrent neural models tailored to forecasting over data streams generated by geospatial point-cloud sources. We design a Dynamic…
Multi-Service Mobile Traffic Forecasting via Convolutional Long Short-Term Memories
Chaoyun Zhang, Marco Fiore, Paul Patras
Network slicing is increasingly used to partition network infrastructure between different mobile services. Precise service-wise mobile traffic forecasting becomes essential in thi…
Driver Behavior Recognition via Interwoven Deep Convolutional Neural Nets with Multi-stream Inputs
Chaoyun Zhang, Rui Li, Woojin Kim +2
Understanding driver activity is vital for in-vehicle systems that aim to reduce the incidence of car accidents rooted in cognitive distraction. Automating real-time behavior recog…
DELMU: A Deep Learning Approach to Maximising the Utility of Virtualised Millimetre-Wave Backhauls
Rui Li, Chaoyun Zhang, Paul Patras +2
Advances in network programmability enable operators to 'slice' the physical infrastructure into independent logical networks. By this approach, each network slice aims to accommod…
Deep Learning in Mobile and Wireless Networking: A Survey
Chaoyun Zhang, Paul Patras, Hamed Haddadi
The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcomi…
Long-Term Mobile Traffic Forecasting Using Deep Spatio-Temporal Neural Networks
Chaoyun Zhang, Paul Patras
Forecasting with high accuracy the volume of data traffic that mobile users will consume is becoming increasingly important for precision traffic engineering, demand-aware network…
ZipNet-GAN: Inferring Fine-grained Mobile Traffic Patterns via a Generative Adversarial Neural Network
Chaoyun Zhang, Xi Ouyang, Paul Patras
Large-scale mobile traffic analytics is becoming essential to digital infrastructure provisioning, public transportation, events planning, and other domains. Monitoring city-wide m…