no code implementations • 16 Apr 2024 • Bin Liu, Siqi Wu, Jin Wang, Xin Deng, Ao Zhou
Specifically, HiGraphDTI learns hierarchical drug representations from triple-level molecular graphs to thoroughly exploit chemical information embedded in atoms, motifs, and molecules.
no code implementations • 15 Apr 2024 • Tong Qiao, Jianlei Yang, Yingjie Qi, Ao Zhou, Chen Bai, Bei Yu, Weisheng Zhao, Chunming Hu
Graph Neural Networks (GNNs) succeed significantly in many applications recently.
no code implementations • 8 Apr 2024 • Ao Zhou, Jianlei Yang, Tong Qiao, Yingjie Qi, Zhi Yang, Weisheng Zhao, Chunming Hu
GCoDE abstracts the device communication process into an explicit operation and fuses the search of architecture and the operations mapping in a unified space for joint-optimization.
no code implementations • 27 Mar 2024 • Ao Zhou, Bin Liu, Jin Wang, Grigorios Tsoumakas
However, the intrinsic class imbalance in multi-label data may bias the model towards majority labels, since samples relevant to minority labels may be underrepresented in each mini-batch.
no code implementations • 1 Mar 2024 • Zeling Zhang, Dongqi Cai, Yiran Zhang, Mengwei Xu, Shangguang Wang, Ao Zhou
Communication overhead is a significant bottleneck in federated learning (FL), which has been exaggerated with the increasing size of AI models.
no code implementations • 18 Oct 2023 • Yingjie Qi, Jianlei Yang, Ao Zhou, Tong Qiao, Chunming Hu
Graph neural networks (GNNs) have gained significant popularity due to the powerful capability to extract useful representations from graph data.
no code implementations • 28 Aug 2023 • Rongjie Yi, Liwei Guo, Shiyun Wei, Ao Zhou, Shangguang Wang, Mengwei Xu
Large Language Models (LLMs) such as GPTs and LLaMa have ushered in a revolution in machine intelligence, owing to their exceptional capabilities in a wide range of machine learning tasks.
no code implementations • 20 Mar 2023 • Ao Zhou, Jianlei Yang, Yingjie Qi, Yumeng Shi, Tong Qiao, Weisheng Zhao, Chunming Hu
Moreover, HGNAS achieves hardware awareness during the GNN architecture design by leveraging a hardware performance predictor, which could balance the GNN model accuracy and efficiency corresponding to the characteristics of targeted devices.
1 code implementation • 15 Jun 2022 • Rongjie Yi, Ting Cao, Ao Zhou, Xiao Ma, Shangguang Wang, Mengwei Xu
DNNs are ubiquitous on edge devices nowadays.
no code implementations • 10 Mar 2022 • Qing Li, Shangguang Wang, Xiao Ma, Ao Zhou, Fangchun Yang
Recently, Low Earth Orbit (LEO) satellites experience rapid development and satellite edge computing emerges to address the limitation of bent-pipe architecture in existing satellite systems.
1 code implementation • 14 Feb 2022 • Qiyang Zhang, Xiang Li, Xiangying Che, Xiao Ma, Ao Zhou, Mengwei Xu, Shangguang Wang, Yun Ma, Xuanzhe Liu
Deploying deep learning (DL) on mobile devices has been a notable trend in recent years.
1 code implementation • 7 Apr 2021 • Ao Zhou, Jianlei Yang, Yeqi Gao, Tong Qiao, Yingjie Qi, Xiaoyi Wang, Yunli Chen, Pengcheng Dai, Weisheng Zhao, Chunming Hu
Graph neural networks (GNN) have achieved state-of-the-art performance on various industrial tasks.
no code implementations • 22 Oct 2020 • Jinliang Yuan, Mengwei Xu, Xiao Ma, Ao Zhou, Xuanzhe Liu, Shangguang Wang
Our proposed FL can accelerate the learning process and reduce the monetary cost with frequent local aggregation in the same LAN and infrequent global aggregation on a cloud across WAN.
no code implementations • 21 Mar 2020 • Dingcheng Yang, Wenjian Yu, Ao Zhou, Haoyuan Mu, Gary Yao, Xiaoyi Wang
In this work, we propose an effective scheme (called DP-Net) for compressing the deep neural networks (DNNs).
no code implementations • 20 Feb 2018 • Ao Zhou, Wei Wang, Ni Chen, Edmund Y. Lam, Byoungho Lee, Guohai Situ
Fourier ptychographi cmicroscopy(FPM) is a newly developed computational imaging technique that can provide gigapixel images with both high resolution (HR) and wide field of view (FOV).