no code implementations • 8 May 2023 • Yuanzhi Cai, Jagannath Aryal, Yuan Fang, Hong Huang, Lei Fan
In this study, the concept of pruning from a supernet is used for the first time to integrate the selection of channel combination and the training of a semantic segmentation network.
no code implementations • 5 May 2023 • Zhihao Wen, Yuan Fang
Text classification is a fundamental problem in information retrieval with many real-world applications, such as predicting the topics of online articles and the categories of e-commerce product descriptions.
no code implementations • 21 Feb 2023 • Trung-Kien Nguyen, Zemin Liu, Yuan Fang
Assuming no type information is given, we define a so-called latent heterogeneous graph (LHG), which carries latent heterogeneous semantics as the node/edge types cannot be observed.
no code implementations • 16 Feb 2023 • Zemin Liu, Xingtong Yu, Yuan Fang, Xinming Zhang
In particular, prompting is a popular alternative to fine-tuning in natural language processing, which is designed to narrow the gap between pre-training and downstream objectives in a task-specific manner.
no code implementations • 11 Feb 2023 • Deyu Bo, Xiao Wang, Yang Liu, Yuan Fang, Yawen Li, Chuan Shi
Graph neural networks (GNNs) have attracted considerable attention from the research community.
no code implementations • 8 Feb 2023 • Zemin Liu, Trung-Kien Nguyen, Yuan Fang
In particular, the varying neighborhood structures across nodes, manifesting themselves in drastically different node degrees, give rise to the diverse behaviors of nodes and biased outcomes.
no code implementations • 7 Feb 2023 • Xingtong Yu, Zemin Liu, Yuan Fang, Xinming Zhang
However, typical GNNs employ a node-centric message passing scheme that receives and aggregates messages on nodes, which is inadequate in complex structure matching for isomorphism counting.
no code implementations • 9 Jan 2023 • Huanyu Bian, Zhilong Jia, Menghan Dou, Yuan Fang, Lei LI, Yiming Zhao, Hanchao Wang, Zhaohui Zhou, Wei Wang, Wenyu Zhu, Ye Li, Yang Yang, Weiming Zhang, Nenghai Yu, Zhaoyun Chen, Guoping Guo
Therefore, based on VQNet 1. 0, we further propose VQNet 2. 0, a new generation of unified classical and quantum machine learning framework that supports hybrid optimization.
no code implementations • 15 Dec 2022 • Yuanzhi Cai, Lei Fan, Yuan Fang
However, it was found in this study that different classes of objects had their preferred resizing scale for more accurate semantic segmentation.
no code implementations • 14 Dec 2022 • Ruichu Cai, Yuxuan Zhu, Xuexin Chen, Yuan Fang, Min Wu, Jie Qiao, Zhifeng Hao
To address the non-identifiability of PNS, we resort to a lower bound of PNS that can be optimized via counterfactual estimation, and propose Necessary and Sufficient Explanation for GNN (NSEG) via optimizing that lower bound.
no code implementations • 30 Dec 2021 • Xuexin Chen, Ruichu Cai, Yuan Fang, Min Wu, Zijian Li, Zhifeng Hao
However, standard GNNs in the neighborhood aggregation paradigm suffer from limited discriminative power in distinguishing \emph{high-order} graph structures as opposed to \emph{low-order} structures.
no code implementations • 27 Oct 2021 • Zemin Liu, Yuan Fang, Chenghao Liu, Steven C. H. Hoi
Ideally, how a node receives its neighborhood information should be a function of its local context, to diverge from the global GNN model shared by all nodes.
no code implementations • 29 Sep 2021 • Xingtong Yu, Zemin Liu, Yuan Fang, Xinming Zhang
At the graph level, we modulate the graph representation conditioned on the query subgraph, so that the model can be adapted to each unique query for better matching with the input graph.
no code implementations • 14 May 2021 • Zhihao Wen, Yuan Fang, Zemin Liu
That is, MI-GNN does not directly learn an inductive model; it learns the general knowledge of how to train a model for semi-supervised node classification on new graphs.
no code implementations • 21 Jan 2021 • Yuan Fang, Ding Wang, Peng Li, Hang Su, Tian Le, Yi Wu, Guo-Wei Yang, Hua-Li Zhang, Zhi-Guang Xiao, Yan-Qiu Sun, Si-Yuan Hong, Yan-Wu Xie, Huan-Hua Wang, Chao Cao, Xin Lu, Hui-Qiu Yuan, Yang Liu
We report growth, electronic structure and superconductivity of ultrathin epitaxial CoSi2 films on Si(111).
Mesoscale and Nanoscale Physics
no code implementations • 1 Jan 2021 • Wenting Zhao, Yuan Fang, Zhen Cui, Tong Zhang, Jian Yang, Wei Liu
In this paper, we propose a simple yet effective graph deformer network (GDN) to fulfill anisotropic convolution filtering on graphs, analogous to the standard convolution operation on images.
no code implementations • 1 Jan 2021 • Chenghao Liu, Tao Lu, Doyen Sahoo, Yuan Fang, Kun Zhang, Steven Hoi
Meta-learning methods learn the meta-knowledge among various training tasks and aim to promote the learning of new tasks under the task similarity assumption.
no code implementations • 10 Dec 2020 • Yung-Kun Liu, Pisin Chen, Yuan Fang, Petr Valenta
Flying plasma mirrors induced by intense lasers has been proposed as a promising way to generate few-cycle EUV or X-ray lasers.
Plasma Physics
no code implementations • 23 Nov 2020 • Peng Li, Baijiang Lv, Yuan Fang, Wei Guo, Zhongzheng Wu, Yi Wu, Cheng-Maw Cheng, Dawei Shen, Yuefeng Nie, Luca Petaccia, Chao Cao, Zhu-An Xu, Yang Liu
Using angle-resolved photoemission spectroscopy (ARPES) and low-energy electron diffraction (LEED), together with density-functional theory (DFT) calculation, we report the formation of charge density wave (CDW) and its interplay with the Kondo effect and topological states in CeSbTe.
Strongly Correlated Electrons Materials Science
no code implementations • 3 Sep 2020 • Yuan Fang, Chunyan Xu, Zhen Cui, Yuan Zong, Jian Yang
In this paper, we propose a spatial transformer point convolution (STPC) method to achieve anisotropic convolution filtering on point clouds.
1 code implementation • 19 Jul 2020 • Sezin Kircali Ata, Min Wu, Yuan Fang, Le Ou-Yang, Chee Keong Kwoh, Xiao-Li Li
Thirdly, an empirical analysis is conducted to evaluate the performance of the selected methods across seven diseases.
no code implementations • ECCV 2020 • Chenghao Liu, Zhihao Wang, Doyen Sahoo, Yuan Fang, Kun Zhang, Steven C. H. Hoi
Meta-learning methods have been extensively studied and applied in computer vision, especially for few-shot classification tasks.
2 code implementations • 15 Jun 2020 • Mandeep Singh, Yuan Fang
Humans are able to comprehend information from multiple domains for e. g. speech, text and visual.
3 code implementations • 17 May 2020 • Sezin Kircali Ata, Yuan Fang, Min Wu, Jiaqi Shi, Chee Keong Kwoh, Xiao-Li Li
Real-world networks often exist with multiple views, where each view describes one type of interaction among a common set of nodes.
no code implementations • 25 Sep 2019 • Chenghao Liu, Tao Lu, Doyen Sahoo, Yuan Fang, Steven C.H. Hoi.
Meta-learning methods learn the meta-knowledge among various training tasks and aim to promote the learning of new tasks under the task similarity assumption.
1 code implementation • The Twenty-Eighth International Joint Conference on Artificial Intelligence Conference 2019 • Duc-Trong Le, Hady W. Lauw, Yuan Fang
Items adopted by a user over time are indicative of the underlying preferences.
1 code implementation • 17 Jun 2017 • Zhe Wang, Kingsley Kuan, Mathieu Ravaut, Gaurav Manek, Sibo Song, Yuan Fang, Seokhwan Kim, Nancy Chen, Luis Fernando D'Haro, Luu Anh Tuan, Hongyuan Zhu, Zeng Zeng, Ngai Man Cheung, Georgios Piliouras, Jie Lin, Vijay Chandrasekhar
Beyond that, we extend the original competition by including text information in the classification, making this a truly multi-modal approach with vision, audio and text.
no code implementations • TACL 2014 • Yuan Fang, Ming-Wei Chang
Microblogs present an excellent opportunity for monitoring and analyzing world happenings.