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- W4319777907 abstract "We introduce sSTNN, a sparse and parallelized version of a spatio-temporal neural network (STNN) that enables training on much larger datasets. First, we introduce the model architecture and discuss the modifications we made to enable the use of a sparse data structure and multi-GPU parallelization. Then we present empirical results that demonstrate sSTNNs ability to train and inference on a dataset 17 times larger than STNN is capable of. Finally, we discuss the effect of sparsification on runtime and present evidence that sSTNN can achieve upwards of 117× reduction in memory usage compared to STNN." @default.
- W4319777907 created "2023-02-11" @default.
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- W4319777907 date "2022-12-17" @default.
- W4319777907 modified "2023-10-10" @default.
- W4319777907 title "Sparse Spatio-Temporal Neural Network for Large-Scale Forecasting" @default.
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- W4319777907 doi "https://doi.org/10.1109/bigdata55660.2022.10036330" @default.
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