A recent article by Theo Wolf has highlighted the Kolmogorov-Arnold Network (KAN), a new type of network making significant advancements in the machine learning (ML) field. Developed by Ziming Liu and his team from MIT, Caltech, and Northeastern, KAN leverages the Kolmogorov-Arnold Theorem to design deep learning networks, challenging well-established methodologies. The network has shown remarkable performance in MRI scan data analysis, consistently outperforming traditional Multi-Layer Perceptrons (MLPs). The paper titled 'CEST-KAN: Kolmogorov-Arnold Networks for CEST MRI Data Analysis' details these findings. Additionally, KAGNNs, which combine Kolmogorov-Arnold Networks with Graph Learning, have been introduced.
KAGNNs: Kolmogorov-Arnold Networks meet Graph Learning. https://t.co/6GHMDVMMWA
I'm excited to have supervised this paper, which found *fascinating* mechanisms LLMs use to decide their confidence in what comes next Entropy neurons work by writing a massive vector to the null space of the unembed, to increase the final LN scale, to make logits more uniform https://t.co/A6Awo3U2ew
Kolmogorov-Arnold network for MRI scan data analysis. #KAN consistently outperformed MLP. “CEST-KAN: Kolmogorov-Arnold Networks for CEST MRI Data Analysis” https://t.co/ctFZbzBVIK
This week we have explained the KAN (Kolmogorov-Arnold Network) paper by @ZimingLiu11 and team from @MIT @Caltech @Northeastern KAN is a novel approach that leverages Kolmogorov-Arnold Theorem to design deep learning networks. By doing this, it challenges the well-established… https://t.co/sYp4RaSUVh
In his recent article, @TheoW0lf shared a new type of network — Kolmogorov-Arnold — that is making waves in the ML world. https://t.co/qKLuEk4C2y