17 citations · 23 across the 8 of their papers we have counts for
8 papers
Efficient Adversarial Training with Robust Early-Bird Tickets
Zhiheng Xi, Rui Zheng, Tao Gui +2
Adversarial training is one of the most powerful methods to improve the robustness of pre-trained language models (PLMs). However, this approach is typically more expensive than tr…
Semantic Communication Enabling Robust Edge Intelligence for Time-Critical IoT Applications
Andrea Cavagna, Nan Li, Alexandros Iosifidis +1
This paper aims to design robust Edge Intelligence using semantic communication for time-critical IoT applications. We systematically analyze the effect of image DCT coefficients o…
Design and Prototyping Distributed CNN Inference Acceleration in Edge Computing
Zhongtian Dong, Nan Li, Alexandros Iosifidis +1
For time-critical IoT applications using deep learning, inference acceleration through distributed computing is a promising approach to meet a stringent deadline. In this paper, we…
Robust Lottery Tickets for Pre-trained Language Models
Rui Zheng, Rong Bao, Yuhao Zhou +6
Recent works on Lottery Ticket Hypothesis have shown that pre-trained language models (PLMs) contain smaller matching subnetworks(winning tickets) which are capable of reaching acc…
Graph Reinforcement Learning-based CNN Inference Offloading in Dynamic Edge Computing
Nan Li, Alexandros Iosifidis, Qi Zhang
This paper studies the computational offloading of CNN inference in dynamic multi-access edge computing (MEC) networks. To address the uncertainties in communication time and Edge…
Edge-Varying Fourier Graph Networks for Multivariate Time Series Forecasting
Kun Yi, Qi Zhang, Liang Hu +4
The key problem in multivariate time series (MTS) analysis and forecasting aims to disclose the underlying couplings between variables that drive the co-movements. Considerable rec…