Generative Causal Interpretation Model for Spatio-Temporal Representation Learning

Author:

Zhao Yu1ORCID,Deng Pan1ORCID,Liu Junting1ORCID,Jia Xiaofeng2ORCID,Zhang Jianwei3ORCID

Affiliation:

1. Beihang University, Beijing, China

2. Beijing Big Data Centre, Beijing, China

3. Capinfo Company Limited, Beijing, China

Publisher

ACM

Reference35 articles.

1. Lei Bai , Lina Yao , Can Li , Xianzhi Wang , and Can Wang . 2020. Adaptive graph convolutional recurrent network for traffic forecasting. Advances in neural information processing systems 33 ( 2020 ), 17804--17815. Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang. 2020. Adaptive graph convolutional recurrent network for traffic forecasting. Advances in neural information processing systems 33 (2020), 17804--17815.

2. Pan Deng , Yu Zhao , Junting Liu , Xiaofeng Jia , and Mulan Wang . 2023. Spatio-temporal neural structural causal models for bike flow prediction. arXiv preprint arXiv:2301.07843 ( 2023 ). Pan Deng, Yu Zhao, Junting Liu, Xiaofeng Jia, and Mulan Wang. 2023. Spatio-temporal neural structural causal models for bike flow prediction. arXiv preprint arXiv:2301.07843 (2023).

3. Laurent Dinh , David Krueger , and Yoshua Bengio . 2014 . Nice: Non-linear independent components estimation. arXiv preprint arXiv:1410.8516 (2014). Laurent Dinh, David Krueger, and Yoshua Bengio. 2014. Nice: Non-linear independent components estimation. arXiv preprint arXiv:1410.8516 (2014).

4. Hadi Mohaghegh Dolatabadi , Sarah Erfani , and Christopher Leckie . 2020 . Invertible generative modeling using linear rational splines . In International Conference on Artificial Intelligence and Statistics. PMLR, 4236--4246 . Hadi Mohaghegh Dolatabadi, Sarah Erfani, and Christopher Leckie. 2020. Invertible generative modeling using linear rational splines. In International Conference on Artificial Intelligence and Statistics. PMLR, 4236--4246.

5. Autonomous inference of complex network dynamics from incomplete and noisy data

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