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This is fascinating, as far as pushing the creative boundaries of how to accomplish IR with neural networks, and showing the potential headroom that's available
by svcrunch 5y ago
This is fascinating, as far as pushing the creative boundaries of how to accomplish IR with neural networks, and showing the potential headroom that's available.
It seems wildly impracticable to productionize at the moment (excepting Google, perhaps). If I'm not misunderstanding, the index is actually built by training a neural network (they use networks ranging from 250M-11B parameters, i.e. 500MB-22GB in size). Still, for an important collection of documents, this might be how it's done in a few years.
- touisteur 5y agoEver since I learned about and implemented a learned index I've been seeing the power of these new things. Don't care much for computer vision, but generic performance improvement primitives, yes please.
- bmc7505 5y ago> It seems wildly impracticable to productionize at the moment (excepting Google, perhaps). There were a couple related papers [1, 2] from Google on learning to index a few years ago. In fact, I'm a little surprised they weren't cited. [1]: https://dl.acm.org/doi/pdf/10.1145/3183713.3196909 https://dl.acm.org/doi/pdf/10.1145/3183713.3196909 [2]: https://arxiv.org/pdf/2012.12501.pdf https://arxiv.org/pdf/2012.12501.pdf
- elcomet 5y agoThis is very different from learning to index I think. This is about storing data in the weights of a model.