400 citations · 524 across the 3 of their papers we have counts for
3 papers
Generating and designing DNA with deep generative models
Nathan Killoran, Leo J. Lee, Andrew Delong +2
We propose generative neural network methods to generate DNA sequences and tune them to have desired properties. We present three approaches: creating synthetic DNA sequences using…
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…