3 citations · 8 across the 7 of their papers we have counts for
7 papers
Cascaded Multi-task Adaptive Learning Based on Neural Architecture Search
Yingying Gao, Shilei Zhang, Zihao Cui +2
Cascading multiple pre-trained models is an effective way to compose an end-to-end system. However, fine-tuning the full cascaded model is parameter and memory inefficient and our…
MPPN: Multi-Resolution Periodic Pattern Network For Long-Term Time Series Forecasting
Xing Wang, Zhendong Wang, Kexin Yang +4
Long-term time series forecasting plays an important role in various real-world scenarios. Recent deep learning methods for long-term series forecasting tend to capture the intrica…
ESCL: Equivariant Self-Contrastive Learning for Sentence Representations
Jie Liu, Yixuan Liu, Xue Han +2
Previous contrastive learning methods for sentence representations often focus on insensitive transformations to produce positive pairs, but neglect the role of sensitive transform…
Adaptive Hybrid Spatial-Temporal Graph Neural Network for Cellular Traffic Prediction
Xing Wang, Kexin Yang, Zhendong Wang +4
Cellular traffic prediction is an indispensable part for intelligent telecommunication networks. Nevertheless, due to the frequent user mobility and complex network scheduling mech…
Network Topology Optimization via Deep Reinforcement Learning
Zhuoran Li, Xing Wang, Ling Pan +5
Topology impacts important network performance metrics, including link utilization, throughput and latency, and is of central importance to network operators. However, due to the c…
GenAD: General Representations of Multivariate Time Seriesfor Anomaly Detection
Xiaolei Hua, Lin Zhu, Shenglin Zhang +5
The reliability of wireless base stations in China Mobile is of vital importance, because the cell phone users are connected to the stations and the behaviors of the stations are d…
Adaptive Multi-receptive Field Spatial-Temporal Graph Convolutional Network for Traffic Forecasting
Xing Wang, Juan Zhao, Lin Zhu +5
Mobile network traffic forecasting is one of the key functions in daily network operation. A commercial mobile network is large, heterogeneous, complex and dynamic. These intrinsic…