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Convolutional Networks on Graphs for Learning Molecular Fingerprints
David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre +4
We introduce a convolutional neural network that operates directly on graphs. These networks allow end-to-end learning of prediction pipelines whose inputs are graphs of arbitrary…
Early Stopping is Nonparametric Variational Inference
Dougal Maclaurin, David Duvenaud, Ryan P. Adams
We show that unconverged stochastic gradient descent can be interpreted as a procedure that samples from a nonparametric variational approximate posterior distribution. This distri…
Gradient-based Hyperparameter Optimization through Reversible Learning
Dougal Maclaurin, David Duvenaud, Ryan P. Adams
Tuning hyperparameters of learning algorithms is hard because gradients are usually unavailable. We compute exact gradients of cross-validation performance with respect to all hype…