KS3 对比 MLP：用 K-Splanifolds 拟合 4D Mandelbulb

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

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独立研究者发布 KS3，即 K-Splanifolds 快速 K 维 hermite 样条流形算法的第三版，在随机采样上用梯度下降拟合 4D Mandelbulb，并与 MLP 及旧版 KS 对比。KS3 收敛更快，在 GPU 上延迟与 MLP 几乎相同。作者称 KS3 在去噪、几何拟合、金融预测上收敛明显快于 MLP，在 LLM 任务上则与 MLP 持平。

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

For the past year I have been studying using a fast K-dim hermite spline manifold algorithm, called K-Splanifolds or KS, for general purpose fast regression. In this video I show the third version, KS3, compared to MLP and older KS versions fitting a 4D mandelbulb via gradient descent on random samples. KS3 converges faster and has nearly identical latency on a GPU as MLP.

KS blocks are suitable for a wide range of regression problems and I have found KS3 ranges from considerably faster convergence as MLP (noise removal, geometric fit, financial prediction) to neck and neck (LLMs). You can view code for all three versions of the algorithm and a paper for KS1 here: https://github.com/curvedinf/k-splanifolds

I am an independent and if anyone would like to explore this with me, let me know. Also if anyone would like to donate access to 8x H100, I would like to attempt a world record nanogpt speedrun with KS3 using configurations I have found beat the WR on proxy hardware.

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