3 ms·
Learning in High Dimension Always Amounts to Extrapolation https://arxiv.org/abs/2110.09485 https://arxiv.org/abs/2110.09485 "Supercharged Interpolation" is n
by famouswaffles 3y ago
Learning in High Dimension Always Amounts to Extrapolation
https://arxiv.org/abs/2110.09485 https://arxiv.org/abs/2110.09485
"Supercharged Interpolation" is not a real thing.
They generalize fine when the data incentivizes that.
- lsh123 3y agoYou always can extrapolate. E.g. linear approximation for x^2 by 2 points will extrapolate reasonably well around these 2 points but will be bad with x -> +/- infinity. Similarly, there are examples where GPT invented legal cases when asked to create a legal brief.
- samus 3y agoI asked GPT 3.5 how to access the Unihan character database[0] using the ICU4J library[1]. GPT 3.5 suggested to use the `com.ibm.icu.util.Unihan` class. It actually doesn't exist, but after pointing that out to it, GPT showed me how to parse the raw database files. [0]: https://www.unicode.org/charts/unihan.html https://www.unicode.org/charts/unihan.html [1]: https://unicode-org.github.io/icu/userguide/icu4j/ https://unicode-org.github.io/icu/userguide/icu4j/