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Neural Ordinary Differential Equations

Neural Ordinary Differential EquationsRicky T. Q. Chen*, Yulia Rubanova*, Jesse Bettencourt*, David DuvenaudUniversity of Toronto, Vector Institute AbstractWe introduce a new family of deep Neural network models. Instead of specifying adiscrete sequence of hidden layers, we parameterize the derivative of the hiddenstate using a Neural network. The output of the network is computed using a black-box Differential equation solver. These continuous-depth models have constantmemory cost, adapt their evaluation strategy to each input, and can explicitly tradenumerical precision for speed. We demonstrate these properties in continuous-depthresidual networks and continuous-time latent variable models. We also constructcontinuous normalizingflows, a generative model that can train by maximumlikelihood, without partitioning or ordering the data dimensions.

Neural Ordinary Differential Equations Ricky T. Q. Chen*, Yulia Rubanova*, Jesse Bettencourt*, David Duvenaud University of Toronto, Vector Institute ... monitor the level of error, and adapt their evaluation strategy on the fly to achieve the requested level of accuracy. This allows the cost of evaluating a model to scale with

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