We are very excited to share a new dataset chock full of interesting triangulations with you. In machine learning, a lot of works try to handle such higher-order inputs, but we show that there is still a long way to go. Let us know what you think!
Great selection of works, but I am missing a lot of references from topology in ML, with the article only assuming a very cursory perspective in terms of 'topology captures connectivity and/or continuity.'
Some works from my colleagues and me go a little bit deeper (no pun intended), for instance:
I agree that this will be biased and I don't think this should be used for any serious type of survey. I was literally just interested in the results. :-)
Fully agree with you there! As I said, I'm merely interested in getting a rough picture of the demographics (INB4: selection bias etc.). I find the statement you mention just as problematic as if you were to switch out the ethnic identity with _any_ form (self-identified) identity. That being said, I don't see anything problematic about looking at population demographics and then asking _why_ some identities are over- or under-represented. This poll should not be used for that, though :-)
Maybe 'assumption' would have been the better choice. Given that HN is (for me!) broadly about playfully engaging with technology and society at large, I would expect that this _should_ attract a relatively diverse audience.
They missed a great opportunity to call this BloTorch (Bayesian Learning and Optimisation?) here, but I'm very excited to see such methods gaining more traction!
We are very excited to share a new dataset chock full of interesting triangulations with you. In machine learning, a lot of works try to handle such higher-order inputs, but we show that there is still a long way to go. Let us know what you think!