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• W3210361503 abstract "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 researchers and machine learning practitioners in a unified easy-to-use framework. PyTorch Geometric Temporal was created with foundations on existing libraries in the PyTorch eco-system, streamlined neural network layer definitions, temporal snapshot generators for batching, and integrated benchmark datasets. These features are illustrated with a tutorial-like case study. Experiments demonstrate the predictive performance of the models implemented in the library on real-world problems such as epidemiological forecasting, ride-hail demand prediction, and web traffic management. Our sensitivity analysis of runtime shows that the framework can potentially operate on web-scale datasets with rich temporal features and spatial structure." @default.
• W3210361503 created "2021-11-08" @default.
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• W3210361503 date "2021-10-26" @default.
• W3210361503 modified "2023-10-18" @default.
• W3210361503 title "PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models" @default.
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• W3210361503 doi "https://doi.org/10.1145/3459637.3482014" @default.
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