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Explaining Grokking Through Circuit Efficiency

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Manage episode 380104052 series 3448051
Content provided by Arize AI. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Arize AI 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.

Join Arize Co-Founder & CEO Jason Lopatecki, and ML Solutions Engineer, Sally-Ann DeLucia, as they discuss “Explaining Grokking Through Circuit Efficiency." This paper explores novel predictions about grokking, providing significant evidence in favor of its explanation. Most strikingly, the research conducted in this paper demonstrates two novel and surprising behaviors: ungrokking, in which a network regresses from perfect to low test accuracy, and semi-grokking, in which a network shows delayed generalization to partial rather than perfect test accuracy.
Find the transcript and more here: https://arize.com/blog/explaining-grokking-through-circuit-efficiency-paper-reading/

To learn more about ML observability, join the Arize AI Slack community or get the latest on our LinkedIn and Twitter.

  continue reading

30 episodes

Artwork
iconShare
 
Manage episode 380104052 series 3448051
Content provided by Arize AI. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Arize AI 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.

Join Arize Co-Founder & CEO Jason Lopatecki, and ML Solutions Engineer, Sally-Ann DeLucia, as they discuss “Explaining Grokking Through Circuit Efficiency." This paper explores novel predictions about grokking, providing significant evidence in favor of its explanation. Most strikingly, the research conducted in this paper demonstrates two novel and surprising behaviors: ungrokking, in which a network regresses from perfect to low test accuracy, and semi-grokking, in which a network shows delayed generalization to partial rather than perfect test accuracy.
Find the transcript and more here: https://arize.com/blog/explaining-grokking-through-circuit-efficiency-paper-reading/

To learn more about ML observability, join the Arize AI Slack community or get the latest on our LinkedIn and Twitter.

  continue reading

30 episodes

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