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Overparameterized neural networks: Feature learning precedes overfitting, research finds

Modern neural networks, with billions of parameters, are so overparameterized that they can "overfit" even random, structureless data. Yet when trained on datasets with structure, they learn the ...
Hackaday

Analyzing Feature Learning In Artificial Neural Networks And Neural Collapse

Artificial Neural Networks (ANNs) are commonly used for machine vision purposes, where they are tasked with object recognition. This is accomplished by taking a multi-layer network and using a ...
Hosted on MSN

Using geometry and physics to explain feature learning in deep neural networks

Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate ...
Ars Technica

Here’s what’s really going on inside an LLM’s neural network

With most computer programs—even complex ones—you can meticulously trace through the code and memory usage to figure out why that program generates any specific behavior or output. That’s generally ...
Phys.org

Common feature between forest fires and neural networks reveals universal framework

Researchers from the University of Tokyo in collaboration with Aisin Corporation have demonstrated that universal scaling laws, which describe how the properties of a system change with size and scale ...
Purdue University

The mind’s eye of a neural network system

A tool for checking the output of neural networks makes finding errors as easy as spotting mountaintops from an airplane. (Purdue University image) WEST LAFAYETTE, Ind. – In the background of image ...