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Implementation code for the paper "Graph Neural Network-Based Anomaly Detection in Multivariate Time Series" (AAAI 2021) - GDN/train.py at main · d-ailin/GDN
The GDN layer can be used as a normal non-linearity in PyTorch but must be instantiated with the number of channels at the application and the torch device where it will be used. The GDN layer supports 4-d inputs (batch of images) or 5-d inputs (batch of videos). The 5-d input is handled by unfolding the sequence dimension.
21 de jun. de 2021 · Implementation code for the paper "Graph Neural Network-Based Anomaly Detection in Multivariate Time Series" (AAAI 2021) - Issues · d-ailin/GDN
We propose Graph Deconvolutional Network (GDN) and motivate the design of GDN via a combination of inverse filters in spectral domain and de-noising layers in wavelet domain, as the inverse operation results in a high frequency amplifier and may amplify the noise. We demonstrate the effectiveness of the proposed method on several tasks ...
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Implementation code for the paper "Graph Neural Network-Based Anomaly Detection in Multivariate Time Series" (AAAI 2021) - GDN/test.py at main · d-ailin/GDN
Implementation code for the paper "Graph Neural Network-Based Anomaly Detection in Multivariate Time Series" (AAAI 2021) - GDN/install.sh at main · d-ailin/GDN
