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License: MIT License
When I run INLP, sometimes it ends up zeroing out the entire representation, rather than only removing the relevant information.
The first iteration is a reasonable projection (with average size 0.001), but thereafter, the average is near 0, like 1e-19. Visually, it seems that the representation after the first iteration could still be further collapsed without zeroing it out. Does anything come to mind?
Hello,
I noticed a line where the return value isn't captured. I've run small experiments with toy data and it didn't seem to matter.
Was the line unnecessary, or should it be:
w_basis = w_basis * np.sign(w_basis[0][0]) # handle sign ambiguity
Hi, the work is very interesting. Could you please send the dataset to me? My email is [email protected]. Thanks!~
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