In this episode I’m joined by Amir Zamir, Postdoctoral researcher at both Stanford & UC Berkeley.
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Amir joins us fresh off of winning the 2018 CVPR Best Paper Award for co-authoring “Taskonomy: Disentangling Task Transfer Learning.” In this work, Amir and his coauthors explore the relationships between different types of visual tasks and use this structure to better understand the types of transfer learning that will be most effective for each, resulting in what they call a “computational taxonomic map for task transfer learning.”
In our conversation, we discuss the nature and consequences of the relationships that Amir and his team discovered, and how they can be used to build more effective visual systems with machine learning. Along the way Amir provides a ton of great examples and explains the various tools his team has created to illustrate these concepts.
This is the final update for the TWIML Online Meetup. Tomorrow, July 17th at 5pm Pacific Time, Nic Teague will lead a discussion on the paper Quantum Machine Learning by Jacob Biamonte and his coauthors, which discusses how to devise and implement concrete quantum software for machine learning tasks. If you haven’t joined our meetup yet, visit twimlai.com/meetup to do so.
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Mentioned in the Interview
- Paper: Taskonomy: Disentangling Task Transfer Learning
- Paper: ImageNet Classification with Deep Convolutional Neural Networks
- Taskonomy API
- TWIML Presents: Series page
- TWIML Events Page
- TWIML Meetup
- TWIML Newsletter
“More On That Later” by Lee Rosevere licensed under CC By 4.0