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 Gradient Descent









SupplementaryMaterial

Neural Information Processing Systems

We adopt four bioinformatics datasets in the experiment. Given the input graph, it will randomly add or cut a certain portion ofconnections between nodes withtheprobability of0.2. It will set the feature of 20% nodes in the graph to Gaussian noises with mean and standard deviation is 0.5. We adopt the Adam [5] optimizer, which is a variant of Stochastic Gradient Descent (SGD) with adaptivemoment estimation.



9226f8122feb9c229c1efd9270ce7021-Paper-Conference.pdf

Neural Information Processing Systems

To unveil how the brain learns, ongoing work seeks biologically-plausible approximations of gradient descent algorithms for training recurrent neural networks (RNNs).