A new model of learning centers on bursts of neural activity that act as teaching signals — approximating backpropagation, the algorithm behind learning in AI. Every time a human or machine learns how ...

Backpropagation is not limited to function derivatives. Any algorithm that effectively takes the loss function and applies gradual, positive changes back through the network is valid.

A new technical paper titled “The backpropagation algorithm implemented on spiking neuromorphic hardware” was published by University of Zurich, ETH Zurich, Los Alamos National Laboratory, Royal ...

Godfather of AI Geoffrey Hinton remains a towering figure in artificial intelligence. Learn about his education, his role as ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Leann Chen explains how knowledge graphs ...

The most common technique used to train neural networks is the back-propagation algorithm. Back propagation requires a value for a parameter called the learning rate. The effectiveness of back ...

The algorithm, called backpropagation, was the spark that fired up the current revolution of deep learning as the de facto machine learning behemoth. At its core, “backprop” is an extremely effective ...

AI transforms RF engineering through neural networks that predict signal behavior and interference patterns, enabling ...