David Duvenaud | Reflecting on Neural ODEs | NeurIPS 2019

Original paper: David’s homepage: ~duvenaud/ Summary: We introduce a new family of deep neural network models. Instead of specifying a discrete sequence of hidden layers, we parameterize the derivative of the hidden state using a neural network. The output of the network is computed using a black-box differential equation solver. These continuous-depth models have constant memory cost, adapt their evaluation strategy to each input, and can explic