KS3 benchmark reports faster convergence than MLP at similar GPU latency
Independent researcher u/1ncehost compared the third version of K-Splanifolds, KS3, with multilayer perceptrons and earlier KS versions while fitting a 4D Mandelbulb through gradient descent on random samples. The author reports that KS3 converged faster and had nearly identical GPU latency to an MLP. The author also says KS3 ranged from considerably faster convergence than an MLP in areas including noise removal, geometric fitting and financial prediction to roughly equal performance for large language models. These are self-reported results that have not been independently verified.
The post provides a concrete KS3-versus-MLP comparison and links to implementations and a paper, offering a research signal that merits independent evaluation.
| 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