Category Archives: Torch

Phil 1.2.20

7:00 – 4:30 ASRC PhD

  • More highlighting and slides. Once I get through the Background section, I’ll write the overview, then repeat that patterns.
    • I’m tweaking too much text to keep the markup version. Sigh.
    • Finished Background and sent that to Wayne
  • GPT-2 Agents. See if we can get multiple texts generated – nope
    • Build a corpus of .txt files
    • Try running them through LMN
  • No NOAA meeting
  • No ORCA meeting

Phil 1.30.19

7:00 – 7:00 ASRC PhD


  • Nice visualization, with map-like aspects: The Climate Learning Tree
  •  Dissertation
    • Start JuryRoom section – done!
    • Finished all content!
  • GPT-2 Agents
    • Download big model and try to run it
    • Move models and code out of the transformers project
  • GOES
    • Learning by Cheating (sounds like a mechanism for simulation to work with)
      • Vision-based urban driving is hard. The autonomous system needs to learn to perceive the world and act in it. We show that this challenging learning problem can be simplified by decomposing it into two stages. We first train an agent that has access to privileged information. This privileged agent cheats by observing the ground-truth layout of the environment and the positions of all traffic participants. In the second stage, the privileged agent acts as a teacher that trains a purely vision-based sensorimotor agent. The resulting sensorimotor agent does not have access to any privileged information and does not cheat. This two-stage training procedure is counter-intuitive at first, but has a number of important advantages that we analyze and empirically demonstrate. We use the presented approach to train a vision-based autonomous driving system that substantially outperforms the state of the art on the CARLA benchmark and the recent NoCrash benchmark. Our approach achieves, for the first time, 100% success rate on all tasks in the original CARLA benchmark, sets a new record on the NoCrash benchmark, and reduces the frequency of infractions by an order of magnitude compared to the prior state of the art. For the video that summarizes this work, see this https URL
  • Meeting with Aaron
    • Overview at the beginning of each chapter – look at Aaron’s chapter 5 for
    • example intro and summary.
    • Callouts in text should match the label
    • hfill to right-justify
    • Footnote goes after puntuation
    • Punctuation goes inside quotes
    • for url monospace use \texttt{} (
    • indent blockquotes 1/2 more tab
    • Non breaking spaces on names
    • Increase figure sizes in intro