[30] Dustin Tran - Probabilistic Programming for Deep Learning
Dustin Tran is a research scientist at Google Brain. His research focuses on advancing science and intelligence, including areas involving probability, programs, and neural networks.
Dustin’s PhD thesis is titled "Probabilistic Programming for Deep Learning", which he completed in 2020 at Columbia University.
We discuss the intersection of probabilistic modeling and deep learning, including the Edward library and the novel inference algorithms and models that he developed in the thesis.
- Episode notes: https://cs.nyu.edu/~welleck/episode30.html
- Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter
- Find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html
- Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview
Dustin’s PhD thesis is titled "Probabilistic Programming for Deep Learning", which he completed in 2020 at Columbia University.
We discuss the intersection of probabilistic modeling and deep learning, including the Edward library and the novel inference algorithms and models that he developed in the thesis.
- Episode notes: https://cs.nyu.edu/~welleck/episode30.html
- Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter
- Find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html
- Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview
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