2 citations · 6 across the 5 of their papers we have counts for
5 papers
Understanding and Improving Deep Graph Neural Networks: A Probabilistic Graphical Model Perspective
Jiayuan Chen, Xiang Zhang, Yinfei Xu +3
Recently, graph-based models designed for downstream tasks have significantly advanced research on graph neural networks (GNNs). GNN baselines based on neural message-passing mecha…
Soft Threshold Ternary Networks
Weixiang Xu, Xiangyu He, Tianli Zhao +3
Large neural networks are difficult to deploy on mobile devices because of intensive computation and storage. To alleviate it, we study ternarization, a balance between efficiency…
APRIL: Finding the Achilles' Heel on Privacy for Vision Transformers
Jiahao Lu, Xi Sheryl Zhang, Tianli Zhao +2
Federated learning frameworks typically require collaborators to share their local gradient updates of a common model instead of sharing training data to preserve privacy. However,…
Joint Channel and Weight Pruning for Model Acceleration on Moblie Devices
Tianli Zhao, Xi Sheryl Zhang, Wentao Zhu +4
For practical deep neural network design on mobile devices, it is essential to consider the constraints incurred by the computational resources and the inference latency in various…
Architecture Aware Latency Constrained Sparse Neural Networks
Tianli Zhao, Qinghao Hu, Xiangyu He +4
Acceleration of deep neural networks to meet a specific latency constraint is essential for their deployment on mobile devices. In this paper, we design an architecture aware laten…