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More Language, Less Labeling with Kate Saenko - #580

2022-06-27 · 47 min · episode 580 · 16 entities

episode page audio

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    More Language, Less Labeling with Kate Saenko - #580
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    Feed author/publisher: Sam Charrington
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    professor at Boston University and
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    professor at Boston University and
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  • → hosted by TWIML company
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    Feed author/publisher: TWIML

Entities found in this episode

companys 7

  • mentioned TWIML company
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    Feed author/publisher: TWIML
  • mentioned Boston University company
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    professor at Boston University and
  • mentioned Technology company
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    Feed category: Technology
  • works at Boston University company
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    evidence rules-v4
    professor at Boston University and
  • works at Boston University company
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    evidence rules-v4
    professor at Boston University and
  • discusses Technology company
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    Feed category: Technology
  • hosted by TWIML company
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    evidence rules-v4
    Feed author/publisher: TWIML

concepts 5

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

persons 3

  • mentioned Kate Saenko person
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    More Language, Less Labeling with Kate Saenko - #580
  • mentioned Sam Charrington person
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    evidence rules-v4
    Feed author/publisher: Sam Charrington
  • hosted by Sam Charrington person
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    evidence rules-v4
    Feed author/publisher: Sam Charrington

podcasts 1

Episode description as stored
Today we continue our CVPR series joined by Kate Saenko, an associate professor at Boston University and a consulting professor for the MIT-IBM Watson AI Lab. In our conversation with Kate, we explore her research in multimodal learning, which she spoke about at the Multimodal Learning and Applications Workshop, one of a whopping 6 workshops she spoke at. We discuss the emergence of multimodal learning, the current research frontier, and Kate’s thoughts on the inherent bias in LLMs and how to deal with it. We also talk through some of the challenges that come up when building out applications, including the cost of labeling, and some of the methods she’s had success with. Finally, we discuss Kate’s perspective on the monopolizing of computing resources for “foundational” models, and her paper Unsupervised Domain Generalization by learning a Bridge Across Domains. The complete show notes for this episode can be found at twimlai.com/go/580