Abstract: Stochastic graph neural networks (SGNNs) are information processing architectures that learn representations from data over random graphs. SGNNs are trained with respect to the expected ...
HIS-GCN-master/ │ README.md │ requirements.txt │ ... │ └───HIS/ │ │ globals.py │ │ HISsampler.py │ │ ... │ │ │ └───pytorch ...
Abstract: Convolution filters in deep convolutional networks display rotation variant behavior. While learned invariant behavior can be partially achieved, this paper shows that current methods of ...
Official implementation of "Stock Recommendations for Individual Investors: A Temporal Graph Network Approach with Mean-Variance Efficient Sampling (ICAIF '24)" The tricky point in stock ...
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