activity
20182022
most citedQuadraLib: A Performant Quadratic Neural Network Library for Architecture Optimization and Design Exploration

14 citations · 24 across the 4 of their papers we have counts for

collaborators

8 papers

cs.LG202214 cited

QuadraLib: A Performant Quadratic Neural Network Library for Architecture Optimization and Design Exploration

Zirui Xu, Fuxun Yu, Jinjun Xiong +1

The significant success of Deep Neural Networks (DNNs) is highly promoted by the multiple sophisticated DNN libraries. On the contrary, although some work have proved that Quadrati…

cs.AR20204 cited

Towards Latency-aware DNN Optimization with GPU Runtime Analysis and Tail Effect Elimination

Fuxun Yu, Zirui Xu, Tong Shen +12

Despite the superb performance of State-Of-The-Art (SOTA) DNNs, the increasing computational cost makes them very challenging to meet real-time latency and accuracy requirements. A…

cs.NI20203 cited

Efficient Neural Network Implementation with Quadratic Neuron

Zirui Xu, Jinjun Xiong, Fuxun Yu +1

Previous works proved that the combination of the linear neuron network with nonlinear activation functions (e.g. ReLu) can achieve nonlinear function approximation. However, simpl…

cs.DC2019

Helios: Heterogeneity-Aware Federated Learning with Dynamically Balanced Collaboration

Zirui Xu, Fuxun Yu, Jinjun Xiong +1

In this paper, we propose Helios, a heterogeneity-aware FL framework to tackle the straggler issue. Helios identifies individual devices' heterogeneous training capability, and the…

cs.CV20193 cited

LanCe: A Comprehensive and Lightweight CNN Defense Methodology against Physical Adversarial Attacks on Embedded Multimedia Applications

Zirui Xu, Fuxun Yu, Xiang Chen

Recently, adversarial attacks can be applied to the physical world, causing practical issues to various Convolutional Neural Networks (CNNs) powered applications. Most existing phy…

cs.CR2019

DoPa: A Comprehensive CNN Detection Methodology against Physical Adversarial Attacks

Zirui Xu, Fuxun Yu, Xiang Chen

Recently, Convolutional Neural Networks (CNNs) demonstrate a considerable vulnerability to adversarial attacks, which can be easily misled by adversarial perturbations. With more a…