Search Results for author: Yuan Fang

Found 28 papers, 5 papers with code

OSTA: One-shot Task-adaptive Channel Selection for Semantic Segmentation of Multichannel Images

no code implementations8 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.

Semantic Segmentation

Augmenting Low-Resource Text Classification with Graph-Grounded Pre-training and Prompting

no code implementations5 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.

Information Retrieval Retrieval +2

Link Prediction on Latent Heterogeneous Graphs

no code implementations21 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.

Link Prediction Representation Learning +1

GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks

no code implementations16 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.

Graph Representation Learning

A Survey on Spectral Graph Neural Networks

no code implementations11 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.

Graph Representation Learning

On Generalized Degree Fairness in Graph Neural Networks

no code implementations8 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.

Fairness Node Classification

Learning to Count Isomorphisms with Graph Neural Networks

no code implementations7 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.


VQNet 2.0: A New Generation Machine Learning Framework that Unifies Classical and Quantum

no code implementations9 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.

Quantum Machine Learning Unity

SBSS: Stacking-Based Semantic Segmentation Framework for Very High Resolution Remote Sensing Image

no code implementations15 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.

Data Augmentation Semantic Segmentation

On the Probability of Necessity and Sufficiency of Explaining Graph Neural Networks: A Lower Bound Optimization Approach

no code implementations14 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.

Motif Graph Neural Network

no code implementations30 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.

Graph Classification Graph Embedding +2

Node-wise Localization of Graph Neural Networks

no code implementations27 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.

Representation Learning

Count-GNN: Graph Neural Networks for Subgraph Isomorphism Counting

no code implementations29 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.


Meta-Inductive Node Classification across Graphs

no code implementations14 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.

Classification General Knowledge +6

Growth, Electronic Structure and Superconductivity of Ultrathin Epitaxial CoSi2 Films

no code implementations21 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

Graph Deformer Network

no code implementations1 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.

Localized Meta-Learning: A PAC-Bayes Analysis for Meta-Learning Beyond Global Prior

no code implementations1 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.


Reflectivity and Spectrum of Relativistic Flying Plasma Mirrors

no code implementations10 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

Charge density wave and weak Kondo effect in a Dirac semimetal CeSbTe

no code implementations23 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

Spatial Transformer Point Convolution

no code implementations3 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.

Dictionary Learning Semantic Segmentation

Recent Advances in Network-based Methods for Disease Gene Prediction

1 code implementation19 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.

Graph Representation Learning

Adaptive Task Sampling for Meta-Learning

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.

Classification General Classification +1

Emotion Recognition in Audio and Video Using Deep Neural Networks

2 code implementations15 Jun 2020 Mandeep Singh, Yuan Fang

Humans are able to comprehend information from multiple domains for e. g. speech, text and visual.

Multimodal Emotion Recognition speech-recognition +2

Multi-View Collaborative Network Embedding

3 code implementations17 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.

Network Embedding

Localized Meta-Learning: A PAC-Bayes Analysis for Meta-Leanring Beyond Global Prior

no code implementations25 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.


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