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Generalization – Interpolation – Extrapolation in Machine Learning: Which is it now!?
Dec 15, 2021
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A summary of the Generalization – Interpolation – Extrapolation debate in machine Learning. “Learning in High Dimension Always Amounts to Extrapolation” explained. Paper: 📜 Balestriero, Randall, Jerome Pesenti, and Yann LeCun. "Learning in High Dimension Always Amounts to Extrapolation." arXiv preprint arXiv:2110.09485 (2021).
https://arxiv.org/abs/2110.09485
🔗 Twitter announcement by Y. LeCun:
https://twitter.com/ylecun/status/1450560732483948545
🔗 Response of F. Chollet:
https://twitter.com/fchollet/status/1450524400227287040
Older Twitter fights: 🍿 🔗 Y. LeCun claiming that in high dimensions everything is extrapolation.
https://twitter.com/ylecun/status/1409940043951742981
🔗 Gary Markus claiming the opposite:
https://twitter.com/GaryMarcus/status/1411401507610796032
🔗 “You Don’t Understand Neural Networks Until You Understand the Universal Approximation Theorem” Blog Post by Andre Ye:
https://medium.com/analytics-vidhya/you-dont-understand-neural-networks-until-you-understand-the-universal-approximation-theorem-85b3e7677126
🔗 Reddit discussion about the paper:
https://www.reddit.com/r/MachineLearning/comments/qbbknr/r_learning_in_high_dimension_always_amounts_to/
Thanks to our Patrons who support us in Tier 2, 3, 4: 🙏 donor, Dres. Trost GbR, Yannik Schneider Outline: 00:00 Paper warning 01:44 It’s interpolation 03:05 No, it’s extrapolation 06:38 Now it’s interpolation again 08:08 What is it?? ▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀ 🔥 Optionally, pay us a coffee to help with our Coffee Bean production! ☕ Patreon:
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00:00
Paper warning
01:44
It’s interpolation
03:05
No, it’s extrapolation
06:38
Now it’s interpolation again
08:08
What is it??
Category: Research Talk
Category: Research Paper
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