Scientific Computing, Applied and Industrial Mathematics (SCAIM) Seminar: Bas Peters

  • Date: 10/29/2019
  • Time: 12:30
Bas Peters, UBC

University of British Columbia


Learning from One or a Few Large-Scale, but Partial Examples: Computational Tools, Regularization, and Network Design


Different from most deep learning settings, we focus on the situation where we have only one or a few large-scale examples. We also assume that only partial labels are available. Examples include video segmentation and segmentation of 3D geophysical imaging results where the ground-truth labels are available in a small number of boreholes.


First, we discuss how we can use partial/projected loss-functions and regularization of the neural-network output to train on partial examples and mitigate low prediction quality away from labeled pixels/voxels. Second, we present a fully reversible network that enables training on large-scale data. We induce and exploit the reversibility of networks based on certain partial-differential-equations. As a result, no storage is required for all the network states to compute a gradient, and the memory requirements for training are independent of network depth. A fully reversible network can train directly from video-to-video without resorting to slice-by-slice based methods and therefore simplifies previous approaches.


Throughout the talk, we highlight similarities and differences with well-known computational techniques from PDE-constrained optimization.


Joint work with Eldad Haber, Justin Granek, and Keegan Lensink.

Other Information: 

Location: ESB 4133 (PIMS lounge)


For more information, please see event webpage here.