jdfr
Joined 10 years ago
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47
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5
Member since
Apr 4
Recent submissions
Comment · 1 year ago
Comment · 6 years ago
The beginning of the text feels really odd, almost GPT-3-like at some points. At least, that's the way it seems when you do not know the jargon. It just rambles on and on. Thankfully it gets more coherent later on, but I am not sure what the author intended to say in this piece.
Comment · 10 years ago
This video (wanderers) does not give a reason, but it sums up quite nicely the way I feel it: https://youtu.be/YH3c1QZzRK4
Comment · 10 years ago
Whoa! Quite similar to the "magic" behind compressed sensing (as noted by the authors of the paper linked by the OP):
https://en.wikipedia.org/wiki/Compressed_sensing
Nice to see that similar mathematical tools are being developed for similar problems.
Edit: typo
Comment · 10 years ago
Vitter's algorithm is for when you need to generate a random list of unique items with sequential reads. If you drop that requirement, you can just use a substitution-permutation network: https://en.wikipedia.org/wiki/Substitution-permutation_netwo...
A SPN will generate a unique random permutation, from which you can draw your random list of unique items. With a little of post-ptocessing, you can generate random permutations of arbitrary size.
I have implemented it in matlab and python:
http://nl.mathworks.com/matlabcentral/fileexchange/36626-alg...
As a result, OP seems to think the model was trained on a lot of Perl: https://xcancel.com/jxmnop/status/1953899440315527273#m
LOL! I think these results speak more to the flexibility of Perl than any actual insight on the training data! After all, 93% of inkblots are valid Perl scripts: https://www.mcmillen.dev/sigbovik/