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[11] Jacob Andreas - Learning from Language

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Manage episode 302418434 series 2982803
Content provided by The Thesis Review and Sean Welleck. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by The Thesis Review and Sean Welleck or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
Jacob Andreas is an Assistant Professor at MIT, where he leads the language and intelligence group, focusing on language as a communicative and computational tool. His PhD thesis is titled "Learning from Language" which he completed in 2018 at UC Berkeley. We discuss compositionality and neural module networks, the intersection of RL and language, and translating a neural communication channel called 'neuralese', and how this can lead to more interpretable machine learning models. Episode notes: https://cs.nyu.edu/~welleck/episode11.html Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter, and find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html Support The Thesis Review at www.buymeacoffee.com/thesisreview
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47 episodes

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Manage episode 302418434 series 2982803
Content provided by The Thesis Review and Sean Welleck. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by The Thesis Review and Sean Welleck or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
Jacob Andreas is an Assistant Professor at MIT, where he leads the language and intelligence group, focusing on language as a communicative and computational tool. His PhD thesis is titled "Learning from Language" which he completed in 2018 at UC Berkeley. We discuss compositionality and neural module networks, the intersection of RL and language, and translating a neural communication channel called 'neuralese', and how this can lead to more interpretable machine learning models. Episode notes: https://cs.nyu.edu/~welleck/episode11.html Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter, and find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html Support The Thesis Review at www.buymeacoffee.com/thesisreview
  continue reading

47 episodes

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