Probabilistic Numeric Convolutional Neural NetworksDownload PDF

28 Sep 2020 (modified: 25 Jan 2021)ICLR 2021 PosterReaders: Everyone
  • Keywords: probabilistic numerics, gaussian processes, discretization error, pde, superpixel, irregularly spaced time series, misssing data, spatial uncertainty
  • Abstract: Continuous input signals like images and time series that are irregularly sampled or have missing values are challenging for existing deep learning methods. Coherently defined feature representations must depend on the values in unobserved regions of the input. Drawing from the work in probabilistic numerics, we propose Probabilistic Numeric Convolutional Neural Networks which represent features as Gaussian processes, providing a probabilistic description of discretization error. We then define a convolutional layer as the evolution of a PDE defined on this GP, followed by a nonlinearity. This approach also naturally admits steerable equivariant convolutions under e.g. the rotation group. In experiments we show that our approach yields a $3\times$ reduction of error from the previous state of the art on the SuperPixel-MNIST dataset and competitive performance on the medical time series dataset PhysioNet2012.
  • One-sentence Summary: We build a neural network which integrates internal discretization error and missing values probabilistically with GPs
  • Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics
  • Supplementary Material: zip
10 Replies