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I wonder how it compares to Google's "Learned Index Structures" https://arxiv.org/abs/1712.01208 https://arxiv.org/abs/1712.01208
by ilija139 8y ago
I wonder how it compares to Google's "Learned Index Structures" https://arxiv.org/abs/1712.01208 https://arxiv.org/abs/1712.01208
- koverstreet 8y agoCompletely different - that paper applies to comparison based indexing.
- stochastic_monk 8y agoI don’t think there’s much to write home about RE: Learned Index Structures since classical structures can soundly outperform at least the trumpeted Google “success story” [1]. It’s hype, not substance. [1]: https://dawn.cs.stanford.edu/2018/01/11/index-baselines/ https://dawn.cs.stanford.edu/2018/01/11/index-baselines/
- jacksmith21006 8y agoCompletely disagree. It is NOT simply about the results but looking at the direction and a new approach. Ultimately it also comes down to the power required to get some task done. Also how can a paper submitted to NIPS be hype?
- stochastic_monk 8y agoAs an idea and an application of machine learning, it’s important and worth exploring. Claiming it’s better is false. That’s a distinction worth making.
- jacksmith21006 8y agoGreat link. I will be curious to see if the Google, Jeff Dean, approach works in real life situations. What is interesting is you can use the TPUs and parallelize the approach. Ultimately it is about using less power to get some work done.