EurekAlert!

A backpropagation algorithm for mechanical neural networks (IMAGE)

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. The ...
EurekAlert!

SEOULTECH researchers develop VFF-Net, a revolutionary alternative to backpropagation that transforms AI training

VFF-Net introduces three new methodologies: label-wise noise labelling (LWNL), cosine similarity-based contrastive loss (CSCL), and layer grouping (LG), addressing the challenges of applying a forward ...
TMCnet

WiMi Explores the Application of Neural Networks in Parameter Optimization for Dual-Field Quantum Key Distribution

WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, announced that they are researching the use of neural ...

In 1986, three scientists improved backpropagation, making neural networks learn effectively from data

A groundbreaking 1986 technique called backpropagation revolutionized artificial intelligence, enabling computers to learn from their mistakes. Previously, AI systems struggled to identify errors in ...
Semiconductor Engineering

Backpropagation Algorithm On Neuromorphic Spiking HW (U. Of Zurich, ETH Zurich, LANL)

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 ...
Quanta Magazine

Novel Architecture Makes Neural Networks More Understandable

“Neural networks are currently the most powerful tools in artificial intelligence,” said Sebastian Wetzel, a researcher at the Perimeter Institute for Theoretical Physics. “When we scale them up to ...