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Composing Graphical Models With Neural Networks with David Duvenaud - TWiML Talk #96

2018-01-15 · 35 min · episode 96 · 13 entities

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    Composing Graphical Models With Neural Networks with David Duvenaud - TWiML Talk #96
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  • → hosted by TWIML company
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Entities found in this episode

concepts 5

  • mentioned Science concept
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  • mentioned Tech News concept
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  • discusses Science concept
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    evidence rules-v4
    Feed category: Science
  • discusses News concept
    0.40
    evidence rules-v4
    Feed category: News
  • discusses Tech News concept
    0.40
    evidence rules-v4
    Feed category: Tech News

companys 4

  • mentioned TWIML company
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    Feed author/publisher: TWIML
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  • hosted by TWIML company
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    Feed author/publisher: TWIML

persons 3

  • mentioned Neural Networks person
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    Composing Graphical Models With Neural Networks with David Duvenaud - TWiML Talk #96
  • mentioned Sam Charrington person
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    evidence rules-v4
    Feed author/publisher: Sam Charrington
  • hosted by Sam Charrington person
    0.55
    evidence rules-v4
    Feed author/publisher: Sam Charrington

podcasts 1

Episode description as stored
In this episode, we hear from David Duvenaud, assistant professor in the Computer Science and Statistics departments at the University of Toronto. David joined me after his talk at the Deep Learning Summit on “Composing Graphical Models With Neural Networks for Structured Representations and Fast Inference.” In our conversation, we discuss the generalized modeling and inference framework that David and his team have created, which combines the strengths of both probabilistic graphical models and deep learning methods. He gives us a walkthrough of his use case which is to automatically segment and categorize mouse behavior from raw video, and we discuss how the framework is applied here and for other use cases. We also discuss some of the differences between the frequentist and bayesian statistical approaches. The notes for this show can be found at twimlai.com/talk/96