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Channel Gating for Cheaper and More Accurate Neural Nets with Babak Ehteshami Bejnordi - #385
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Sam Charrington

· 55 Minutes

PodcastThe TWIML AI Podcast

Channel Gating for Cheaper and More Accurate Neural Nets with Babak Ehteshami Bejnordi - #385

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Today we’re joined by Babak Ehteshami Bejnordi, a Research Scientist at Qualcomm. Babak is currently focused on conditional computation, which is the main driver for today’s conversation. We dig into a few papers in great detail including one from this year’s CVPR conference, Conditional Channel Gated Networks for Task-Aware Continual Learning, covering how gates are used to drive efficiency and accuracy, while decreasing model size, how this research manifests into actual products, and more!

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This story was published by The TWIML AI Podcast and written by Sam Charrington. SyncAI.news shows a preview; the complete article is on the publisher's site.

Read the full story on twimlai.com

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