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DTSTART:19700308T020000
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DTSTAMP:20260522T150121Z
LOCATION:D165
DTSTART;TZID=America/Chicago:20181111T114500
DTEND;TZID=America/Chicago:20181111T120000
UID:submissions.supercomputing.org_SC18_sess147_ws_cafcw108@linklings.com
SUMMARY:Scalable Deep Ensemble Learning for Cancer Drug Discovery
DESCRIPTION:Sam Ade Jacobs, Tim Moon, and Brian C. Van Essen (Lawrence Liv
 ermore National Laboratory)\n\nIn this work, we demonstrate how the Liverm
 ore Tournament Fast Batch (LTFB) ensemble algorithm is able to efficiently
  tune hyperparameters and accelerate the time to solution for several canc
 er drug discovery networks.  Drawn from the DOE-NCI Pilot 1 and ECP CANDLE
  projects we show significantly improved training quality for the "Uno" da
 ta set and associated network and a dramatic reduction in the wall-clock t
 ime for training the "Combo" network to a fixed level of convergence.  LTF
 B is an ensemble method that creates a set of neural network models and tr
 ains each instance of these models in parallel. Periodically, each model s
 elects another model to pair with, exchanges models, and then run a local 
 tournament against held-out tournament datasets. The winning model will co
 ntinue training on the local training datasets. LTFB is implemented in the
  Livermore Big Artificial Neural Network toolkit (LBANN), a toolkit optimi
 zed for composing multiple levels of parallelism on HPC architectures.\n\n
 Tag: Applications, Deep Learning, Exascale\n\nRegistration Category: Works
 hop Reg Pass\n\nSession Chairs: Thomas J. Barr (Nationwide Children's Hosp
 ital); Patricia Kovatch (Icahn School of Medicine at Mount Sinai); and Eri
 c Stahlberg (MD Anderson Cancer Center, University of Texas)\n\n
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