Search Results for author: Xin Cao

Found 20 papers, 5 papers with code

Grad-Align+: Empowering Gradual Network Alignment Using Attribute Augmentation

no code implementations23 Aug 2022 Jin-Duk Park, Cong Tran, Won-Yong Shin, Xin Cao

Network alignment (NA) is the task of discovering node correspondences across different networks.

GSim: A Graph Neural Network based Relevance Measure for Heterogeneous Graphs

no code implementations12 Aug 2022 Linhao Luo, Yixiang Fang, Moli Lu, Xin Cao, Xiaofeng Zhang, Wenjie Zhang

Most of existing relevance measures focus on homogeneous networks where objects are of the same type, and a few measures are developed for heterogeneous graphs, but they often need the pre-defined meta-path.

Community Detection Graph Mining +1

Improved Binary Forward Exploration: Learning Rate Scheduling Method for Stochastic Optimization

no code implementations9 Jul 2022 Xin Cao

A new gradient-based optimization approach by automatically scheduling the learning rate has been proposed recently, which is called Binary Forward Exploration (BFE).

Stochastic Optimization

BFE and AdaBFE: A New Approach in Learning Rate Automation for Stochastic Optimization

no code implementations6 Jul 2022 Xin Cao

In this paper, a new gradient-based optimization approach by automatically adjusting the learning rate is proposed.

Stochastic Optimization

Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting

1 code implementation18 Jun 2022 Zezhi Shao, Zhao Zhang, Wei Wei, Fei Wang, Yongjun Xu, Xin Cao, Christian S. Jensen

However, intuitively, traffic data encompasses two different kinds of hidden time series signals, namely the diffusion signals and inherent signals.

Graph Learning Time Series Forecasting +1

Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System

1 code implementation19 Apr 2022 Ding Zou, Wei Wei, Xian-Ling Mao, Ziyang Wang, Minghui Qiu, Feida Zhu, Xin Cao

Different from traditional contrastive learning methods which generate two graph views by uniform data augmentation schemes such as corruption or dropping, we comprehensively consider three different graph views for KG-aware recommendation, including global-level structural view, local-level collaborative and semantic views.

Contrastive Learning Data Augmentation +2

BCOT: A Markerless High-Precision 3D Object Tracking Benchmark

no code implementations CVPR 2022 Jiachen Li, Bin Wang, Shiqiang Zhu, Xin Cao, Fan Zhong, Wenxuan Chen, Te Li, Jason Gu, Xueying Qin

Our new benchmark dataset contains 20 textureless objects, 22 scenes, 404 video sequences and 126K images captured in real scenes.

3D Object Tracking Object Tracking

Machine Learning Solar Wind Driving Magnetospheric Convection in Tail Lobes

no code implementations3 Feb 2022 Xin Cao, Jasper S. Halekas, Stein Haaland, Suranga Ruhunusiri, Karl-Heinz Glassmeier

To quantitatively study the driving mechanisms of magnetospheric convection in the magnetotail lobes on a global scale, we utilize data from the ARTEMIS spacecraft in the deep tail and the Cluster spacecraft in the near tail.

BIG-bench Machine Learning

On the Power of Gradual Network Alignment Using Dual-Perception Similarities

no code implementations26 Jan 2022 Jin-Duk Park, Cong Tran, Won-Yong Shin, Xin Cao

Network alignment (NA) is the task of finding the correspondence of nodes between two networks based on the network structure and node attributes.

KS-GNN: Keywords Search over Incomplete Graphs via Graphs Neural Network

no code implementations NeurIPS 2021 Yu Hao, Xin Cao, Yufan Sheng, Yixiang Fang, Wei Wang

Keyword search is a fundamental task to retrieve information that is the most relevant to the query keywords.

Off-Dynamics Inverse Reinforcement Learning from Hetero-Domain

no code implementations21 Oct 2021 Yachen Kang, Jinxin Liu, Xin Cao, Donglin Wang

To achieve this, the widely used GAN-inspired IRL method is adopted, and its discriminator, recognizing policy-generating trajectories, is modified with the quantification of dynamics difference.

Continuous Control reinforcement-learning

MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction

1 code implementation Findings (ACL) 2022 Linhan Zhang, Qian Chen, Wen Wang, Chong Deng, Shiliang Zhang, Bing Li, Wei Wang, Xin Cao

In this work, we propose a novel unsupervised embedding-based KPE approach, Masked Document Embedding Rank (MDERank), to address this problem by leveraging a mask strategy and ranking candidates by the similarity between embeddings of the source document and the masked document.

Contrastive Learning Document Embedding +4

Multi Point-Voxel Convolution (MPVConv) for Deep Learning on Point Clouds

no code implementations28 Jul 2021 Wei Zhou, Xin Cao, Xiaodan Zhang, Xingxing Hao, Dekui Wang, Ying He

Extensive experiments on benchmark datasets such as ShapeNet Part, S3DIS and KITTI for various tasks show that MPVConv improves the accuracy of the backbone (PointNet) by up to \textbf{36\%}, and achieves higher accuracy than the voxel-based model with up to \textbf{34}$\times$ speedups.

Unsupervised Segmentation for Terracotta Warrior with Seed-Region-Growing CNN(SRG-Net)

no code implementations28 Jul 2021 Yao Hu, Guohua Geng, Kang Li, Wei Zhou, Xingxing Hao, Xin Cao

Then we present a supervised segmentation and unsupervised reconstruction networks to learn the characteristics of 3D point clouds.

Multi Voxel-Point Neurons Convolution (MVPConv) for Fast and Accurate 3D Deep Learning

no code implementations30 Apr 2021 Wei Zhou, Xin Cao, Xiaodan Zhang, Xingxing Hao, Dekui Wang, Ying He

Extensive experiments on benchmark datasets such as ShapeNet Part, S3DIS and KITTI for various tasks show that MVPConv improves the accuracy of the backbone (PointNet) by up to 36%, and achieves higher accuracy than the voxel-based model with up to 34 times speedup.

Edgeless-GNN: Unsupervised Representation Learning for Edgeless Nodes

no code implementations12 Apr 2021 Yong-Min Shin, Cong Tran, Won-Yong Shin, Xin Cao

We study the problem of embedding edgeless nodes such as users who newly enter the underlying network, while using graph neural networks (GNNs) widely studied for effective representation learning of graphs.

Network Embedding

Inductive Link Prediction for Nodes Having Only Attribute Information

1 code implementation16 Jul 2020 Yu Hao, Xin Cao, Yixiang Fang, Xike Xie, Sibo Wang

In attributed graphs, both the structure and attribute information can be utilized for link prediction.

Inductive Link Prediction

Monotonic Cardinality Estimation of Similarity Selection: A Deep Learning Approach

no code implementations15 Feb 2020 Yaoshu Wang, Chuan Xiao, Jianbin Qin, Xin Cao, Yifang Sun, Wei Wang, Makoto Onizuka

The feature extraction model transforms original data and threshold to a Hamming space, in which a deep learning-based regression model is utilized to exploit the incremental property of cardinality w. r. t.

Management regression

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