KS3基准测试称收敛快于MLP且GPU延迟相近
AI 总结
独立研究者u/1ncehost将K-Splanifolds第三版KS3与多层感知机及早期KS版本进行比较，测试其在随机样本上通过梯度下降拟合4D Mandelbulb。作者称KS3收敛更快，GPU延迟与多层感知机近乎相同，并表示在去噪、几何拟合和金融预测等领域，KS3的收敛速度明显快于多层感知机，而在大语言模型任务上则大致持平。这些均为作者自述、尚未经独立验证的结果。
深度见解
该帖子提供了KS3与多层感知机的具体对比，并公开了实现和论文链接，构成值得进一步独立评测的研究信号。
| 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. submitted by /u/1ncehost [link] [comments] |
#KS3#K-Splanifolds#Multilayer Perceptron#NanoGPT#NVIDIA H100