18 citations · 28 across the 6 of their papers we have counts for
10 papers
From Quantum Graph Computing to Quantum Graph Learning: A Survey
Yehui Tang, Junchi Yan, Hancock Edwin
Quantum computing (QC) is a new computational paradigm whose foundations relate to quantum physics. Notable progress has been made, driving the birth of a series of quantum-based a…
PyramidTNT: Improved Transformer-in-Transformer Baselines with Pyramid Architecture
Kai Han, Jianyuan Guo, Yehui Tang +1
Transformer networks have achieved great progress for computer vision tasks. Transformer-in-Transformer (TNT) architecture utilizes inner transformer and outer transformer to extra…
Homogeneous Architecture Augmentation for Neural Predictor
Yuqiao Liu, Yehui Tang, Yanan Sun
Neural Architecture Search (NAS) can automatically design well-performed architectures of Deep Neural Networks (DNNs) for the tasks at hand. However, one bottleneck of NAS is the p…
Augmented Shortcuts for Vision Transformers
Yehui Tang, Kai Han, Chang Xu +4
Transformer models have achieved great progress on computer vision tasks recently. The rapid development of vision transformers is mainly contributed by their high representation a…
Vision Transformer Pruning
Mingjian Zhu, Yehui Tang, Kai Han
Vision transformer has achieved competitive performance on a variety of computer vision applications. However, their storage, run-time memory, and computational demands are hinderi…
Manifold Regularized Dynamic Network Pruning
Yehui Tang, Yunhe Wang, Yixing Xu +4
Neural network pruning is an essential approach for reducing the computational complexity of deep models so that they can be well deployed on resource-limited devices. Compared wit…