The article "Building and Visualising Kolmogorov–Arnold Networks" explores the concept of neural networks that learn functions rather than just weights. It discusses the construction and visualization of Kolmogorov–Arnold networks, providing insights into how these networks operate and how they can be represented visually. The post was originally published on Towards Data Science.
arXiv:2610.20117v1 Announce Type: new Abstract: KANLab is a browser-based visualization tool designed to interactively teach the core concepts of Kolmogorov-Arnold Networks (KAN), forked from the wid...
The intuition behind neural networks and why they need activation functions. The post Neural Networks, Explained for Beginners: Start Here If They’ve Confused You appeared first on Towards Data Science .
The paper investigates the sample‑efficiency of Kolmogorov‑Arnold Networks (KANs) in reinforcement learning. Using experiments on the Feynman dataset and Gymnasium benchmarks, the authors find that KANs achieve comparable performance to standard Multi‑Layer Perceptrons while requiring 40% fewer samples, with up to 50% relative performance gains during training. These improvements hold even when reward noise varies, underscoring KANs’ potential for more sample‑efficient RL.
Let's discover how neural networks learn, step by step The post Backpropagation Explained for Beginners (Part 1): Building the Intuition appeared first on Towards Data Science .
arXiv:2607. 18290v1 Announce Type: cross Abstract: In recent years, Kolmogorov-Arnold Networks (KANs) have attracted increasing attention due to their effectiveness in machine learning and scientific computing tasks, offering a new paradigm for neural network design.
The article discusses how artificial neural networks can develop their own learning mechanisms without explicit guidance from designers. It highlights that such self-organizing networks may provide insights into the brain’s ability to reorganize itself during learning.
Kolmogorov Arnold networks (KAN) have recently been introduced as a (deep) neural network architecture whose trainable parameters adapt the activation functions, instead of the coefficients of the affine transformations at the core of traditional architectures such as deep multilayer perceptrons (MLPs). This architecture builds on the Kolmogorov-Arnold theorem, which endows it with universal approximation properties.
arXiv:2606. 27126v1 Announce Type: new Abstract: Kolmogorov Arnold networks (KAN) have recently been introduced as a (deep) neural network architecture whose trainable parameters adapt the activation functions, instead of the coefficients of the affine transformations at the core of traditional architectures such as deep multilayer perceptrons (MLPs).
arXiv:2604. 21174v3 Announce Type: replace-cross Abstract: Kolmogorov-Arnold Networks (KANs) replace fixed activations with learnable univariate edge functions whose behavior depends strongly on the chosen basis.
The paper studies Kolmogorov‑Arnold Networks (KANs), a neural architecture that treats activation functions as learnable components, offering improved interpretability for scientific applications. It investigates how KANs scale with dataset size on image classification tasks (MNIST, Fashion‑MNIST) and a magnetic‑parameter regression task, revealing a broken neural scaling law that transitions from a faster to a slower decay of test loss as data grows. The authors also analyze how the learned activation functions evolve from simple linear approximations to more complex, interpretable symbolic forms as more data is provided.
The article "Beyond Point Predictions: A Practical Introduction to Bayesian Neural Networks" discusses how Bayesian neural networks enable more informed decision-making by quantifying uncertainty in predictions. It introduces practical aspects of implementing these models and highlights their advantages over traditional point prediction approaches.
These lecture notes accompany the PhD course "Physics Informed Neural Network" offered at the University of Bozen/Bolzano in 2025/2026. They introduce Physics Informed Deep Neural Networks (PINNs) and Neural Operators (NOs), covering implementation from scratch in PyTorch and with libraries such as NVIDIA PhysicsNeMo. The notes also discuss advanced topics like Mixture-of-Models, Fourier Neural Operators, and Physics‑Informed Kolmogorov‑Arnold Networks (PIKANs).