activity
20192022
most citedTo Learn or Not to Learn: Deep Learning Assisted Wireless Modem Design

3 citations · 8 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2022★ 2 cited

Rethinking the Number of Shots in Robust Model-Agnostic Meta-Learning

Xiaoyue Duan, Guoliang Kang, Runqi Wang +4

Robust Model-Agnostic Meta-Learning (MAML) is usually adopted to train a meta-model which may fast adapt to novel classes with only a few exemplars and meanwhile remain robust to a…

cs.GT2022★ 1 cited

Competitive Online Truthful Time-Sensitive-Valued Data Auction

Shuangshuang Xue, Xiang-Yang Li

In this work, we investigate online mechanisms for trading time-sensitive valued data. We adopt a continuous function to represent the data value fluctuation over time .…

cs.CV2022

Associative Adversarial Learning Based on Selective Attack

Runqi Wang, Xiaoyue Duan, Baochang Zhang +4

A human's attention can intuitively adapt to corrupted areas of an image by recalling a similar uncorrupted image they have previously seen. This observation motivates us to improv…

cs.CV2020★ 2 cited

Anti-Bandit Neural Architecture Search for Model Defense

Hanlin Chen, Baochang Zhang, Song Xue +4

Deep convolutional neural networks (DCNNs) have dominated as the best performers in machine learning, but can be challenged by adversarial attacks. In this paper, we defend against…

eess.SP2019★ 3 cited

To Learn or Not to Learn: Deep Learning Assisted Wireless Modem Design

S. Xue, A. Li, J. Wang +4

Deep learning is driving a radical paradigm shift in wireless communications, all the way from the application layer down to the physical layer. Despite this, there is an ongoing d…