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LOCATION:Track 2
DTSTART;TZID=America/New_York:20201117T100000
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UID:submissions.supercomputing.org_SC20_sess179@linklings.com
SUMMARY:Constraints and Physics in Machine Learning
DESCRIPTION:Paper\n\nA Parallel Framework for Constraint-Based Bayesian Ne
 twork Learning via Markov Blanket Discovery\n\nSrivastava, Chockalingam, A
 luru\n\nBayesian networks (BNs) are a widely used graphical model in machi
 ne learning. As learning the structure of BNs is NP-hard, high-performance
  computing methods are necessary for constructing large-scale networks. In
  this paper, we present a parallel framework to scale BN structure learnin
 g algorithms...\n\n---------------------\nMeshfreeFlowNet: A Physics-Const
 rained Deep Continuous Space-Time Super-Resolution Framework\n\nJiang, Esm
 aeilzadeh, Azizzadenesheli, Kashinath, Mustafa...\n\nWe propose MeshfreeFl
 owNet, a novel deep learning framework, to generate continuous (grid-free)
  spatiotemporal solutions from the low-resolution inputs. While being comp
 utationally efficient, MeshfreeFlowNet accurately recovers the fine-scale 
 quantities of interest. MeshfreeFlowNet allows for: (i) t...\n\n----------
 -----------\nRecurrent Neural Network Architecture Search for Geophysical 
 Emulation\n\nMaulik, Egele, Lusch, Balaprakash\n\nDeveloping surrogate geo
 physical models from data is a key research topic in atmospheric and ocean
 ic modeling because of the large computational costs associated with numer
 ical simulation methods. Researchers have started applying a wide range of
  machine learning models, in particular neural network...\n\n\nTag: Applic
 ations, Machine Learning, Deep Learning and Artificial Intelligence, Paral
 lel Programming Languages, Libraries, and Models\n\nRegistration Category:
  Tech Program Reg Pass
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