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I was just about to ask why not use a sphere? since it could be thought of as a nn, it will be into NN someday. guess the splitting and merge can be compared wi
by ewngzen 3y ago
I was just about to ask why not use a sphere? since it could be thought of as a nn, it will be into NN someday. guess the splitting and merge can be compared with dropout then.
- magicalhippo 3y agoI'm no expert, but my immediate thoughts are that evaluating a gaussian blob is very simple, it's just an exponential of a distance. The edge of a sphere makes it more complicated to compute, hence slower. For backpropagation, the differentials of a gaussian is smooth while it's not for a sphere, again because of the edge. Now, if you want to use a sphere you probably will do something like using an opacity falloff similar to ReLU[1], making it transparent at the edge. This should make smooth enough as such I guess, but I imagine you still have the more complicated rendering. Though I may be mistaken. [1]: https://en.wikipedia.org/wiki/Rectifier_(neural_networks) https://en.wikipedia.org/wiki/Rectifier_(neural_networks)