PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models-Reference-Cited by-同舟云学术

PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models

Published:2021-10-26 Issue: Volume: Page:
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Container-title:Proceedings of the 30th ACM International Conference on Information & Knowledge Management
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Author:

Rozemberczki Benedek¹,Scherer Paul²,He Yixuan³,Panagopoulos George⁴,Riedel Alexander⁵,Astefanoaei Maria⁶,Kiss Oliver⁷,Beres Ferenc⁸,López Guzmán⁹,Collignon Nicolas¹⁰,Sarkar Rik¹¹

Affiliation:

1. AstraZeneca, Cambridge, United Kingdom

2. University of Cambridge, Cambridge, United Kingdom

3. University of Oxford, Oxford, United Kingdom

4. École Polytechnique, Paris, France

5. Ernst-Abbe University for Applied Sciences, Jena, Germany

6. IT University of Copenhagen Denmark, Coppenhagen, Denmark

7. Central European University, Budapest, Hungary

8. ELKH SZTAKI, Budapest, Hungary

9. Tryolabs, Montevideo, Uruguay

10. Pedal Me, London, United Kingdom

11. University of Edinburgh, Edinburgh, United Kingdom

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3459637.3482014

Reference70 articles.

1. Sean Anderson Eric Ward Lewis Barnett and Philippina English. 2018. sdmTMB: Spatial and spatiotemporal GLMMs with TMB. https://github.com/pbs-assess/sdmTMB. Sean Anderson Eric Ward Lewis Barnett and Philippina English. 2018. sdmTMB: Spatial and spatiotemporal GLMMs with TMB. https://github.com/pbs-assess/sdmTMB.

2. Node embeddings in dynamic graphs

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