[42] Charles Sutton - Efficient Training Methods For Conditional Random Fields The Thesis Review podcast

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Science Thesis Review Sean Welleck

Innhold levert av The Thesis Review and Sean Welleck. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av The Thesis Review and Sean Welleck eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.

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[42] Charles Sutton - Efficient Training Methods for Conditional Random Fields

2+ y ago 1:18:01

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Innhold levert av The Thesis Review and Sean Welleck. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av The Thesis Review and Sean Welleck eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.

Charles Sutton is a Research Scientist at Google Brain and an Associate Professor at the University of Edinburgh. His research focuses on deep learning for generating code and helping people write better programs. Charles' PhD thesis is titled "Efficient Training Methods for Conditional Random Fields", which he completed in 2008 at UMass Amherst. We start with his work in the thesis on structured models for text, and compare/contrast with today's large language models. From there, we discuss machine learning for code & the future of language models in program synthesis. - Episode notes: https://cs.nyu.edu/~welleck/episode42.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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47 episoder

#Science #Thesis Review #Sean Welleck

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[42] Charles Sutton - Efficient Training Methods for Conditional Random Fields

The Thesis Review

published 2+ y ago

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MP3•Episoder hjem

Innhold levert av The Thesis Review and Sean Welleck. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av The Thesis Review and Sean Welleck eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.

Charles Sutton is a Research Scientist at Google Brain and an Associate Professor at the University of Edinburgh. His research focuses on deep learning for generating code and helping people write better programs. Charles' PhD thesis is titled "Efficient Training Methods for Conditional Random Fields", which he completed in 2008 at UMass Amherst. We start with his work in the thesis on structured models for text, and compare/contrast with today's large language models. From there, we discuss machine learning for code & the future of language models in program synthesis. - Episode notes: https://cs.nyu.edu/~welleck/episode42.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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47 episoder

#Science #Thesis Review #Sean Welleck

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