7 citations · 17 across the 7 of their papers we have counts for
10 papers
FastGL: A GPU-Efficient Framework for Accelerating Sampling-Based GNN Training at Large Scale
Zeyu Zhu, Peisong Wang, Qinghao Hu +3
Graph Neural Networks (GNNs) have shown great superiority on non-Euclidean graph data, achieving ground-breaking performance on various graph-related tasks. As a practical solution…
Accumulated Trivial Attention Matters in Vision Transformers on Small Datasets
Xiangyu Chen, Qinghao Hu, Kaidong Li +2
Vision Transformers has demonstrated competitive performance on computer vision tasks benefiting from their ability to capture long-range dependencies with multi-head self-attentio…
MixFormer: Mixing Features across Windows and Dimensions
Qiang Chen, Qiman Wu, Jian Wang +5
While local-window self-attention performs notably in vision tasks, it suffers from limited receptive field and weak modeling capability issues. This is mainly because it performs…
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…
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…
Generative Zero-shot Network Quantization
Xiangyu He, Qinghao Hu, Peisong Wang +1
Convolutional neural networks are able to learn realistic image priors from numerous training samples in low-level image generation and restoration. We show that, for high-level im…