Wei Ye

Orcid: 0000-0001-8096-0922

Affiliations:
  • University of Minnesota, Department of Computer Science and Engineering, Minneapolis, MN, USA


According to our database1, Wei Ye authored at least 12 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Network embedding based on high-degree penalty and adaptive negative sampling.
Data Min. Knowl. Discov., 2024

The Case for Boosting Mobile Application QoE via Smart Band Switching in 5G/xG Networks.
Proceedings of the 25th International Workshop on Mobile Computing Systems and Applications, 2024

2023
A Comprehensive Real-World Evaluation of 5G Improvements over 4G in Low- and Mid-Bands.
CoRR, 2023

Mid-Band 5G: A Measurement Study in Europe and US.
CoRR, 2023

An In-Depth Measurement Analysis of 5G mmWave PHY Latency and Its Impact on End-to-End Delay.
Proceedings of the Passive and Active Measurement - 24th International Conference, 2023

A Closer Look at Stand-Alone 5G Deployments from the UE Perspective.
Proceedings of the IEEE International Mediterranean Conference on Communications and Networking, 2023

Multi-Modal Vehicle Data Delivery via Commercial 5G Mobile Networks: An Initial Study.
Proceedings of the 43rd IEEE International Conference on Distributed Computing Systems, 2023

2022
Vivisecting mobility management in 5G cellular networks.
Proceedings of the SIGCOMM '22: ACM SIGCOMM 2022 Conference, Amsterdam, The Netherlands, August 22, 2022

5GNN: extrapolating 5G measurements through GNNs.
Proceedings of the 1st International Workshop on Graph Neural Networking, 2022

Raven: belady-guided, predictive (deep) learning for in-memory and content caching.
Proceedings of the 18th International Conference on emerging Networking EXperiments and Technologies, 2022

2021
Accelerating Distributed Deep Learning using Multi-Path RDMA in Data Center Networks.
Proceedings of the SOSR '21: The ACM SIGCOMM Symposium on SDN Research, Virtual Event, USA, October 11, 2021

2018
Laplacian Centrality Peaks Clustering Based on Potential Entropy.
IEEE Access, 2018


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