Fitting a 4D Mandelbulb with KS3 vs MLP [R]

Reddit r/MachineLearning Tools

Summary

A video comparing KS3, the latest hermite spline regression block in the K-Splanifolds series, against MLPs on fitting a 4D Mandelbulb over 9,000 gradient descent steps, with KS3 achieving the lowest loss at nearly identical forward/backward latency.

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# Fitting a 4D Mandelbulb with KS3 vs MLP [R] **Channel:** 1ncehost Source: [https://www.youtube.com/watch?v=coMcHZ2fFfU](https://www.youtube.com/watch?v=coMcHZ2fFfU) ## Description KS3 is an upgraded version of the K-Splanifolds series of fast hermite spline regression blocks. This video shows 9,000 steps of gradient descent backprop training on random samples of a 4D mandelbulb. KS3 is compared to MLP and the earlier versions of K-Splanifolds, and reaches lowest loss by the end of the series. All of the K-Splanifolds series are general purpose regression blocks that can replace MLPs in any existing machine learning algorithm. KS3 has been found to broadly fit faster than MLP while having the nearly identical forward and backwards latency.

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