HN user
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 much data is considered small? What is the complexity of the dataset itself?
Even in Machine Learning it is possible to learn from small datasets without transfer learning. See meta-learning for instance.
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 the original work.
There are always assumptions. At least with public code and models those assumptions are laid bare for all to see and potentially expose any bad assumptions.
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 problem in NP and be done with it.
That's correct. See https://en.wikipedia.org/wiki/Universal_approximation_theore... for more details
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 github or Google search will reveal other GPU accelerated SVM training.
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
Software Engineer
I have been working on machine learning problems in computer vision and natural language processing for my PhD over the past 5 years.