1 code implementation • 18 Jul 2023 • Xiaoqi Wang, Jian Xiong, Hao Gao, Weisi Lin
Finally, quality prediction is obtained by aggregating the subjective scores of the retrieved instances.
1 code implementation • 15 Sep 2022 • Xiaoqi Wang, Han-Wei Shen
In this paper, we propose a model-agnostic model-level explanation method for different GNNs that follow the message passing scheme, GNNInterpreter, to explain the high-level decision-making process of the GNN model.
no code implementations • 13 Jun 2022 • Xiaoqi Wang, Kevin Yen, Yifan Hu, Han-Wei Shen
There are a few existing methods that have attempted to develop a flexible solution for optimizing different aesthetic aspects measured by different aesthetic criteria.
no code implementations • 16 May 2022 • Yang Liu, Xiaoqi Wang, Xi Wang, Zhen Wang, Jürgen Kurths
We assume that the state of a number of nodes in a network could be investigated if necessary, and study what configuration of those nodes could facilitate a better solution for the diffusion-source-localization (DSL) problem.
2 code implementations • 12 Jan 2022 • Xiaoqi Wang, Yingjie Cheng, Yaning Yang, Yue Yu, Fei Li, Shaoliang Peng
Therefore, we conjecture that the multimodal and local-global combination strategies can be treated as the guideline of multi-task SSL for drug discovery.
no code implementations • 22 Aug 2021 • Liangrui Pan, Boya Ji, Peng Xi, Xiaoqi Wang, Mitchai Chongcheawchamnan, Shaoliang Peng
Diabetic retinopathy(DR) is the main cause of blindness in diabetic patients.
no code implementations • 27 Jun 2021 • Xiaoqi Wang, Kevin Yen, Yifan Hu, Han-Wei Shen
In this paper, we propose a Convolutional Graph Neural Network based deep learning framework, DeepGD, which can draw arbitrary graphs once trained.
no code implementations • 2 Apr 2018 • Weicheng Cai, Zexin Cai, Xiang Zhang, Xiaoqi Wang, Ming Li
A novel learnable dictionary encoding layer is proposed in this paper for end-to-end language identification.
no code implementations • 2 Apr 2018 • Weicheng Cai, Zexin Cai, Wenbo Liu, Xiaoqi Wang, Ming Li
After comparing with the state-of-the-art GMM i-vector methods, we give insights into CNN, and reveal its role and effect in the whole pipeline.