Search Results for author: Haoyan Xu

Found 9 papers, 3 papers with code

Multivariate Time Series Classification with Hierarchical Variational Graph Pooling

no code implementations12 Oct 2020 Ziheng Duan, Haoyan Xu, Yueyang Wang, Yida Huang, Anni Ren, Zhongbin Xu, Yizhou Sun, Wei Wang

Then we combine GNNs and our proposed variational graph pooling layers for joint graph representation learning and graph coarsening, after which the graph is progressively coarsened to one node.

Decoder General Classification +6

MTHetGNN: A Heterogeneous Graph Embedding Framework for Multivariate Time Series Forecasting

no code implementations19 Aug 2020 Yueyang Wang, Ziheng Duan, Yida Huang, Haoyan Xu, Jie Feng, Anni Ren

To characterize complex relations among variables, a relation embedding module is designed in MTHetGNN, where each variable is regarded as a graph node, and each type of edge represents a specific static or dynamic relationship.

Decision Making Graph Embedding +2

Parallel Extraction of Long-term Trends and Short-term Fluctuation Framework for Multivariate Time Series Forecasting

no code implementations18 Aug 2020 Yifu Zhou, Ziheng Duan, Haoyan Xu, Jie Feng, Anni Ren, Yueyang Wang, Xiaoqian Wang

In this paper, a MTS forecasting framework that can capture the long-term trends and short-term fluctuations of time series in parallel is proposed.

Decision Making Multi-Task Learning +2

TIMME: Twitter Ideology-detection via Multi-task Multi-relational Embedding

1 code implementation2 Jun 2020 Zhiping Xiao, Weiping Song, Haoyan Xu, Zhicheng Ren, Yizhou Sun

However, the incompleteness of the labels and the features in social network datasets is tricky, not to mention the enormous data size and the heterogeneousity.

CoSimGNN: Towards Large-scale Graph Similarity Computation

no code implementations14 May 2020 Haoyan Xu, Runjian Chen, Yueyang Wang, Ziheng Duan, Jie Feng

In this paper, we focus on similarity computation for large-scale graphs and propose the "embedding-coarsening-matching" framework CoSimGNN, which first embeds and coarsens large graphs with adaptive pooling operation and then deploys fine-grained interactions on the coarsened graphs for final similarity scores.

3D Action Recognition Graph Similarity

Improved object recognition using neural networks trained to mimic the brain's statistical properties

1 code implementation25 May 2019 Callie Federer, Haoyan Xu, Alona Fyshe, Joel Zylberberg

To test this, we trained DCNNs on a composite task, wherein networks were trained to: a) classify images of objects; while b) having intermediate representations that resemble those observed in neural recordings from monkey visual cortex.

Object Object Categorization +2

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