xksteven

4 ms·

1. 1.▲
   by xksteven 4y ago

   I'd be careful of over applying the "bias-variance tradeoff." How to define the variance of a model is not a simple task. I wouldn't say it is immediately obvious how bias-variance relates to small data scenarios. How m
2. 2.▲
   by xksteven 5y ago

   Sometimes reimplantation is impossible without the code and the paper goes on to win awards because it's by a famous scientist. Then if the reimplantation doesn't work most of the time the graduate students are blamed instead of t
3. 3.▲
   by xksteven 6y ago

   My understanding of the read was to show "how" they're equivalent as opposed to how to actually construct such an approximator or learn it. Similar to showing a problem falls in NP, you can reduce the problem down to another
4. 4.▲
   by xksteven 6y ago

   That's correct. See https://en.wikipedia.org/wiki/Universal_approximation_theore... for more details
5. 5.▲
   by xksteven 6y ago

   SVMs are solved via convex optimization methods which have taken more time to get on the GPU train. On the other hand there are GPU accelerated SVM training such as: https://github.com/Xtra-Computing/thundersvm A githu
6. 6.▲
   by xksteven 6y ago

   Location: Miami, Florida Remote: Yes Willing to relocate: Yes Technologies: Python, Node, Pytorch, Tensorflow Résumé/CV: https://stevenbas.art/resources/resume/ Email: xksteven@uchicago.edu Sof