Excited to share our latest research on using Kolmogorov-Arnold Networks (KANs) to predict drug-gene associations of HDAC1 inhibitors in periodontitis. Cutting-edge AI meets periodontal therapy! 🦷✨ Check it out here 👉 DOI: 10.1016/j.compbiolchem.2025.108451

#AI is evolving. Kolmogorov–Arnold Networks (#KANs) offer a novel architecture, now being explored for use with encrypted data via Fully Homomorphic Encryption (#FHE). Read our article for a detailed breakdown of the ideas, motivations, and use cases behind this approach.

🌟 Exciting advancements in the realm of multi-task learning! We delve into the integration of learnable and interpretable modules, specifically Kolmogorov-Arnold Networks (KAN) and graph-based representations, within a pre-trained GPT-2 model. This exploration aims to enhance

KANs (Kolmogorov-Arnold Networks) were introduced with excitement, leveraging the Kolmogorov-Arnold theorem to promise efficiency and interpretability in neural networks. However, they haven’t lived up to the hype. They don’t consistently outperform traditional MLPs and often

@USDesci ResearchHub Kolmogorov-Arnold Networks (KANs) is another interesting option in the context of explainable AI.

"Harnessing Interpretability and Efficiency with Kolmogorov–Arnold Networks in Machine Learning" - engrxiv.org/preprint/view/… #engineering

Kolmogorov-Arnold Networks: Approximation and Learning Guarantees for Functions and their Derivatives. arxiv.org/abs/2504.15110

KAGNNs: Kolmogorov-Arnold Networks meet Graph Learning Roman Bresson, Giannis Nikolentzos, George Panagopoulos et al.. Action editor: Chuxu Zhang. openreview.net/forum?id=03UB1… #graph #node #gnns

Check out! April's latest OAJPE technical papers, free open access👇 ⚡ Physics-Informed Kolmogorov-Arnold Networks for Power System Dynamics: bit.ly/4ixGh6b 💻 Advancing Coherent Power Grid Partitioning: A Review Embracing Machine and Deep Learning:

Graph Kolmogorov-Arnold Networks for Multi-Cancer Classification and Biomarker Identification, An Interpretable Multi-Omics Approach. arxiv.org/abs/2503.22939

FiberKAN: Kolmogorov-Arnold Networks for Nonlinear Fiber Optics. arxiv.org/abs/2504.18833

On the Robustness of Kolmogorov-Arnold Networks: An Adversarial Perspective Tal Alter, Raz Lapid, Moshe Sipper. Action editor: Pin-Yu Chen. openreview.net/forum?id=uafxq… #adversarial #imagenet #classification

It turns out Kolmogorov Arnold Networks are very closely related to classic feedforward neural networks. In the piecewise linear case, we show how to transform your MLP into a KAN and vice versa 🔁 Come by AISTATS Poster Session 1 tomorrow for more details!

Reflecting on a year of AI advancements, the latest "Mixture of Experts" episode dives into Meta's LlamaCon, governance shifts, and emerging technologies like Kolmogorov-Arnold Networks. 🌐 #LlamaCon #ArtificialIntelligence #USA link: ift.tt/LeNp4zW

📄 Enhancing Physics-Informed Neural Networks with a Hybrid Parallel Kolmogorov-Arnold and MLP Architecture by Zuyu Xu et al. #HPC #HighPerformanceComputing #ParallelComputing arxiv.org/abs/2503.23289…

Writing my bachelor's thesis on Kolmogorov-Arnold Convolutional Neural Networks (KAN CNNs) and caught myself thinking... Essentially, each KAN consists of a series of compositions and additions of splines. Splines are, in turn, polynomial-like structures: they simply equal to

Amazing fact: you can build any continuous function just by adding and composing a few fixed continuous functions, plus one extra continuous function that depends on your target function. This is the Kolmogorov-Arnold Theorem, and it disproves the 13th Hilbert conjecture :)

Symbolic Regression with Multimodal Large Language Models and Kolmogorov Arnold Networks. arxiv.org/abs/2505.07956