Statistical Learning
DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling
Graph Convolutional Networks (GCNs) have shown to be effective in handling unordered data like point clouds and meshes.
Appendix for When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting Code for E PI FNP and wILI dataset is publicly available
Deep learning is also suitable because it provides the capability of ingesting data from multiple sources, which better informs the model of what is happening on the ground. Our work aims to close this gap in the literature. Existing approaches for uncertainty quantification can be categorized into three lines. The second line tries to combine the stochastic processes and DNNs. The third line is based on model ensembling [24] which trains multiple DNNs with different initializations and use their predictions for uncertainty quantification.
Appendix: Inverse Learning of Symmetries 1 Model 1.1 Implementation
Having generated novel SMILES with potentially identical druglikeliness, we now perform a self-consistency check. That is, we feed the generated SMILES into our model and predict the properties.
Knowledge-Adaptation Priors Mohammad Emtiyaz Khan
'USPS' digits with neural networks show that K-priors (red square) obtain solutions close to the batch
