NIPS Proceedingsβ

David K. Duvenaud

13 Papers

  • Efficient Graph Generation with Graph Recurrent Attention Networks (2019)
  • Latent Ordinary Differential Equations for Irregularly-Sampled Time Series (2019)
  • Neural Networks with Cheap Differential Operators (2019)
  • Residual Flows for Invertible Generative Modeling (2019)
  • Isolating Sources of Disentanglement in Variational Autoencoders (2018)
  • Neural Ordinary Differential Equations (2018)
  • Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference (2017)
  • Composing graphical models with neural networks for structured representations and fast inference (2016)
  • Probing the Compositionality of Intuitive Functions (2016)
  • Convolutional Networks on Graphs for Learning Molecular Fingerprints (2015)
  • Probabilistic ODE Solvers with Runge-Kutta Means (2014)
  • Active Learning of Model Evidence Using Bayesian Quadrature (2012)
  • Additive Gaussian Processes (2011)