activity
20182022
most citedTowards Efficient Data-Centric Robust Machine Learning with Noise-based Augmentation

2 citations · 2 across the 3 of their papers we have counts for

collaborators

5 papers

cs.IT2022

Reliability and Latency Analysis of Sliding Network Coding With Re-Transmission

Fangzhou Wu, Zhiyuan Tan, Huiying Zhu +1

Future networks are expected to support various ultra-reliable low-latency communications via wireless links. To avoid the loss of packets and keep the low latency, sliding network…

cs.LG20222 cited

Towards Efficient Data-Centric Robust Machine Learning with Noise-based Augmentation

Xiaogeng Liu, Haoyu Wang, Yechao Zhang +2

The data-centric machine learning aims to find effective ways to build appropriate datasets which can improve the performance of AI models. In this paper, we mainly focus on design…

cs.IT2019

Computing and Communicating Functions in Disorganized Wireless Networks

Fangzhou Wu, Li Chen, Nan Zhao +3

For future wireless networks, enormous numbers of interconnections are required, creating a disorganized topology and leading to a great challenge in data aggregation. Instead of c…

cs.IT2018

Computation Over NOMA: Improved Achievable Rate Through Sub-Function Superposition

Fangzhou Wu, Li Chen, Nan Zhao +3

Massive numbers of nodes will be connected in future wireless networks. This brings great difficulty to collect a large amount of data. Instead of collecting the data individually,…

cs.IT2018

Computation over Wide-Band MAC: Improved Achievable Rate through Sub-Function Allocation

Fangzhou Wu, Li Chen, Nan Zhao +3

Future networks are expected to connect an enormous number of nodes wirelessly using wide-band transmission. This brings great challenges. To avoid collecting a large amount of dat…