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This is pretty cool, pardon my limited understanding, trying to see if I get this correctly: 1) they train a model to do sorting, it actually sorts correctly.
by spyckie2 6y ago
This is pretty cool, pardon my limited understanding, trying to see if I get this correctly:
1) they train a model to do sorting, it actually sorts correctly.
2) they optimize the model on efficiency and it becomes better than many custom sort functions?
If I remember correctly from school, you can basically speed up any kind of sorting by using more space (basically by using a hash table). Is the neural network just using the normal sort algo until it sees a pattern in the input and then skips the sort algo for the pattern output? Or to put it plainly, is it just a smarter (or dumber, depending on the size of the neural network) hash table?
- radicalbyte 6y agoThe question is how does the algorithm perform on average, what are the pathological cases and how slow are they? How do you prove that with an algorithm that no human can reason about? Basically this paper looks like they've found an algorithm which is efficient for given sets or given classes of sets. Whether that generalizes is a different problem. It's basically a cool automatic heuristic generator.
- collyw 6y ago> How do you prove that with an algorithm that no human can reason about? This is something I see becoming a problem in the future for machine learning.
- seek3r00 6y agoExactly, that’s what I thought. Although, they should be able to look at the set of instructions that generated the output, which is basically an algorithm in itself. Then they could try to prove whether that algorithm would really generalise.
- deleted 6y ago[deleted]
- deleted 6y ago[deleted]
- seek3r00 6y agoThe model is given the input and a set of instructions (e.g. swap) for producing the output. Essentially, it’s taking a permutation of the instructions that minimise the running time, based on some underlying patterns in the data.