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

Autor: Nicolas Collignon, Benedek Rozemberczki, Rik Sarkar, Yixuan He, Paul Scherer, Alexander Riedel, Maria Astefanoaei, Guzmán López, Ferenc Béres, George Panagopoulos, Oliver Kiss

Publikováno v: Rozemberczki, B, Scherer, P, He, Y, Panagopoulos, G, Riedel, A, Astefanoaei, M S, Kiss, O, Béres, F, López, G, Collignon, N & Sarkar, R 2021, PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models . in G Demartini, G Zuccon, J S Culpepper, Z Huang & H Tong (eds), CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1-5, 2021 . Association for Computing Machinery, pp. 4564-4573 . https://doi.org/10.1145/3459637.3482014
CIKM
Rozemberczki, B, Scherer, P, He, Y, Panagopoulos, G, Riedel, A, Astefanoaei, M, Kiss, O, Beres, F, López, G, Collignon, N & Sarkar, R 2021, PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models . in CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management . Association for Computing Machinery (ACM), pp. 4564–4573, 30th ACM International Conference on Information and Knowledge Management, Gold Coast, Queensland, Australia, 1/11/21 . https://doi.org/10.1145/3459637.3482014

We present PyTorch Geometric Temporal a deep learning framework combining state-of-the-art machine learning algorithms for neural spatiotemporal signal processing. The main goal of the library is to make temporal geometric deep learning available for

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