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The argument that this is 90% of what matters in ML seems a bit bold. AFAICT it is completely missing reinforcement learning, which has been source of some mind
by js8 2y ago
The argument that this is 90% of what matters in ML seems a bit bold. AFAICT it is completely missing reinforcement learning, which has been source of some mindblowing results (from Deepmind) in the past decade. It is also missing other stuff that I as a layman found fascinating (graph neural networks, 1.5 bit networks, ..). Also, I am not sure how much the Kolmogorov complexity actually matters, it's more philosophical than practical. So beware, it is a very opinionated list.
- PartiallyTyped 2y ago> AFAICT it is completely missing reinforcement learning There's no RL in there, nor diffusion. There are many other very interesting papers that I think have lots more to offer — but I just have a master's degree here. I am not a phd let alone a distinguished researcher.
- pmelendez 2y ago>The argument that this is 90% of what matters in ML seems a bit bold. AFAICT it is completely missing reinforcement learning, Wouldn't that account for the other 10%? To me it sounds like the quote is not too far off when you consider that he said "of what matters today". There are many other things missing from that list that might become more relevant tomorrow.