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...
By Lukas Niehaus, Robin Rawiel, Ulf Krumnack
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 .
By Nikhil Dasari
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.
By Kevin Riehl, Shaimaa K. El-Baklish, Fan Wu, Anastasios Kouvelas
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 .
By Nikhil Dasari
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.
By Hoang-Thang Ta
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.
By Jennifer Michalowski | McGovern Institute for Brain Research