arXiv:2605.05780v2 Announce Type: replace
Abstract: In the mid-twentieth century, mathematician and polymath John von Neumann created a computational system on an array of cells as a simple model of...
By Shekhar S. Chandra
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.
By Sam Everest
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
arXiv:2511. 10119v4 Announce Type: replace Abstract: We propose a new perspective for approaching artificial general intelligence (AGI) through an intelligence foundation model (IFM).
By Borui Cai, Yao Zhao
The paper investigates how multilayer perceptrons (MLPs) learn features in regression tasks with clustered data. It finds that instead of forming a single global low‑dimensional representation, MLPs develop monosemantic specialized neurons—each neuron aligns strongly with a specific predictive feature relevant to a particular region of the input space. This specialization results in a collection of local low‑dimensional representations, giving MLPs a provable data‑efficiency advantage over methods that rely on a global representation.
By Amirhesam Abedsoltan, Enric Boix-Adsera, Fivos Kalogiannis, Mikhail Belkin
arXiv:2501. 02436v5 Announce Type: replace Abstract: Advancements in artificial intelligence call for a deeper understanding of the fundamental mechanisms underlying deep learning.
By Yuchen Lin, Yong Zhang, Sihan Feng, Hong Zhao