Phil 5.7.19

7:00 – 8:00 ASRC NASA GOES-R

  • Via CSAIL: “The team’s approach isn’t particularly efficient now – they must train and “prune” the full network several times before finding the successful subnetwork. However, MIT professor Michael Carbin says that his team’s findings suggest that, if we can determine precisely which part of the original network is relevant to the final prediction, scientists might one day be able to skip this expensive process altogether. Such a revelation has the potential to save hours of work and make it easier for meaningful models to be created by individual programmers and not just huge tech companies.”
    • From the abstract of The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
      : We find that a standard pruning technique naturally uncovers subnetworks whose initializations made them capable of training effectively. Based on these results, we articulate the “lottery ticket hypothesis:” dense, randomly-initialized, feed-forward networks contain subnetworks (“winning tickets”) that – when trained in isolation – reach test accuracy comparable to the original network in a similar number of iterations. The winning tickets we find have won the initialization lottery: their connections have initial weights that make training particularly effective. 
    • Sounds like a good opportunity for evolutionary systems
  • Finished with text mods for IEEE letter
  • Added Kaufman and Olfati-Sabir to the discussion on Social Influence Horizon
  • Started the draft deck for the tech summit
  • More MatrixScalar
    • Core functions work
    • Change test and train within the class to input and target
    • Create a coordinating class that loads and creates test and train matrices
  • JuryRoom meeting
    • Progress is good enough to start tracking it. Going to create a set of Google sheets that keep track of tasks and bugs