|
|
|
|
|
|
by dreamlessfate
1350 days ago
|
|
|
Yeah I've come across ReLU, and Leaky ReLU, tanh, Maxout, ELU. I guess I really, really need someone to explain to me like I'm a total idiot why I'd want to use a 2D function on data that might be (and probably is) multidimensional and interdependent. >"The big trouble with probabilities is that, potentially, every event is contingent on every other event and the joint probability distribution of all possible inputs and outputs is a huge dimensional space." Maybe! But this seems like a way more interesting challenge, with the potential to handle way more dimensions, and be way closer to "reality" and "truth", whatever those definitions really are or really mean. >" It's not good enough to estimate that A has a 80% probability of being true, in general you need to estimate what the probability of A is if B is true and C is false." Doesn't that still seem a little lame though? And massively error prone? Filtering problems through a stochastic process of probabilities kinda feels like trying to rig a pachinko machine. |
|
|
As for reasoning with uncertainty people do a lot of that. There was that time I went to my doc because I had rashes on on my arms and neck and my doc (e.g. like MYCIN) thought some of them looked allergic and some looked bacterial. Instead of overthinking it, he wrote me scripts for two different creams and I tried both.
My take is that rapid progress in neural networks is not about people having a lot of understanding or a general theory or even that the average work is of very good quality, but an awful lot of people are working on it and throwing a lot of resources at it. People are discovering things empirically that work.
One can consider many counterfactuals which would have sped up development a lot. If the Japanese had developed a 5th generation computer project around accelerating neural networks instead of accelerating logical inference, if people had tried ReLU in the 1980s, etc.