Geoffrey Hinton, professor at the University of Toronto and engineering fellow at Google Brain, recently published a paper on the Forward-Forward algorithm (FF), a technique for training neural networ ...

Build your own backpropagation algorithm from scratch using Python — perfect for hands-on learners! An Air India Boeing 787 flying to London with over 240 people on board crashed shortly after ...

In early December, dozens of alternatives to traditional backpropagation were proposed during a workshop at the NeurIPS 2020 conference, which took place virtually.

Back Propagation is a common method of training artificial neural networks so as to minimize objective function. This paper describes the implementation of back propagation algorithm.

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 ...

Backpropagation For Softmax — Complete Math Derivation Explained. Posted: May 7, 2025 | Last updated: July 11, 2025. This deep dive covers the full mathematical derivation of softmax gradients ...

AlphaTensor has already identified a new algorithm with which matrix multiplications can be carried out faster than before, as the research team explains in a paper published in the magazine Nature.

Obtaining the gradient of what's known as the loss function is an essential step to establish the backpropagation algorithm developed by University of Michigan researchers to train a material.

Neural networks using the backpropagation algorithm were biologically “unrealistic in almost every respect” he said. For one thing, neurons mostly send information in one direction.