arXiv AI
Oct 7
Von Neumann Networks
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...
How artificial neural networks build their own learning machinery
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.
Related stories
Towards Data Science
Oct 28
Building and Visualising Kolmogorov–Arnold Networks
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.
Towards Data Science
Jun 22
Neural Networks, Explained for Beginners: Start Here If They’ve Confused You
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 .
arXiv AI
Aug 11
Intelligence Foundation Model: A New Perspective to Approach Artificial General Intelligence
arXiv:2511. 10119v4 Announce Type: replace Abstract: We propose a new perspective for approaching artificial general intelligence (AGI) through an intelligence foundation model (IFM).
arXiv Machine Learning
Sep 11
Revenge of Monosemanticity: Neuron Specialization as a New Form of Feature Learning in MLPs
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.
arXiv Machine Learning
Jul 9
Network Dynamics-Based Framework for Understanding Deep Neural Networks
arXiv:2501. 02436v5 Announce Type: replace Abstract: Advancements in artificial intelligence call for a deeper understanding of the fundamental mechanisms underlying deep learning.
arXiv AI
Sep 1
From the Loss Landscape to Diverse Feature Learning in Neural Networks
arXiv:2608.28948v1 Announce Type: cross Abstract: Over the course of the last decade, neural networks have grown from an academic curiosity to moving the markets of nations. Despite this explosion in...
arXiv Machine Learning
Oct 28
FlowGenesis: Representation-Driven Self-Organization of Neural Architectures
FlowGenesis is a framework that lets neural networks self‑organize their architecture during training by adding or removing neurons based on a local statistic called Representation Flux. Neurons with high Flux are split to preserve function, while low‑Flux, low‑activity neurons are pruned. Experiments on image and text classification show that networks starting from different sizes converge to similar neuron counts and performance, and the method also improves continual learning by reducing parameters for new tasks without sacrificing accuracy.
arXiv Machine Learning
Aug 26
Revenge of Monosemanticity: Specialized Neurons Improve Data Efficiency in MLPs
arXiv:2608.24007v1 Announce Type: new Abstract: Understanding how neural networks learn and organize features is central to understanding their behavior. Much existing theory of feature learning has...
Towards Data Science
Jul 19
Backpropagation Explained for Beginners (Part 1): Building the Intuition
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 .
MIT News AI
Oct 23
Q&A: How AI came to model the “messy” human mind
Assistant Professor Benjamin Lindquist explores how early AI engineers intentionally added randomness to models, leading to deep‑learning systems that can hallucinate. The article examines the origins of this design choice and its impact on how AI mimics the human mind’s unpredictability.
arXiv Machine Learning
Sep 1
Perforated Backpropagation: A Neuroscience Inspired Extension to Artificial Neural Networks
arXiv:2501.18018v2 Announce Type: replace-cross Abstract: The neurons of artificial neural networks were originally invented when much less was known about biological neurons than is known today. Our...