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| | tiao.io
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| | We propose a framework that lifts the capabilities of graph convolutional networks (GCNs) to scenarios where no input graph is given and increases their robustness to adversarial attacks. We formulate a joint probabilistic model that considers a prior distribution over graphs along with a GCN-based likelihood and develop a stochastic variational inference algorithm to estimate the graph posterior and the GCN parameters jointly. To address the problem of propagating gradients through latent variables draw...
| | andrew222651.com
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| | proceedings.neurips.cc
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| | neptune.ai
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| You can apply the key ideas of this "Google Collab-friendly" approach to many other base models and tasks.