activity
20152026
most citedA Survey of Millimeter Wave (mmWave) Communications for 5G: Opportunities and Challenges

153 citations · 828 across the 33 of their papers we have counts for

collaborators
Showing cs.LGShow all

7 papers · 1 filter

cs.LG2024

PateGail: A Privacy-Preserving Mobility Trajectory Generator with Imitation Learning

Huandong Wang, Changzheng Gao, Yuchen Wu +3

Generating human mobility trajectories is of great importance to solve the lack of large-scale trajectory data in numerous applications, which is caused by privacy concerns. Howeve…

cs.LG2023

Detecting Vulnerable Nodes in Urban Infrastructure Interdependent Network

Jinzhu Mao, Liu Cao, Chen Gao +4

Understanding and characterizing the vulnerability of urban infrastructures, which refers to the engineering facilities essential for the regular running of cities and that exist n…

cs.LG2023

Spatio-temporal Diffusion Point Processes

Yuan Yuan, Jingtao Ding, Chenyang Shao +2

Spatio-temporal point process (STPP) is a stochastic collection of events accompanied with time and space. Due to computational complexities, existing solutions for STPPs compromis…

cs.LG202329 cited

Learning to Simulate Daily Activities via Modeling Dynamic Human Needs

Yuan Yuan, Huandong Wang, Jingtao Ding +2

Daily activity data that records individuals' various types of activities in daily life are widely used in many applications such as activity scheduling, activity recommendation, a…

cs.LG2021

Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution

Fuxian Li, Jie Feng, Huan Yan +3

Traffic prediction is the cornerstone of an intelligent transportation system. Accurate traffic forecasting is essential for the applications of smart cities, i.e., intelligent tra…

cs.LG202120 cited

Learnable Embedding Sizes for Recommender Systems

Siyi Liu, Chen Gao, Yihong Chen +2

The embedding-based representation learning is commonly used in deep learning recommendation models to map the raw sparse features to dense vectors. The traditional embedding manne…