Semiconductor Engineering

In Situ Backpropagation Strategy That Progressively Updates Neural Network Layers Directly in HW (TU Eindhoven)

A new technical paper titled “Hardware implementation of backpropagation using progressive gradient descent for in situ training of multilayer neural networks” was published by researchers at ...
Nature

Feasibility of short-term hospital mask demand forecasting using a backpropagation neural network under data scarcity

Early-stage infectious disease outbreaks impose acute pressure on hospital supply systems, particularly for essential protective materials such as medical masks. Accurate short-term demand forecasting ...
Nature

Training all-mechanical neural networks for task learning through in situ backpropagation

Recent advances unveiled physical neural networks as promising machine learning platforms, offering faster and more energy-efficient information processing. Compared with extensively-studied optical ...
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 ...
EurekAlert!

Not so simple machines: Cracking the code for materials that can learn

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 ...
PC Magazine

AI backpropagation

The method used to train a large language model (LLM). An AI model's neural network learns by recognizing patterns in the data and constantly adjusting its neurons to predict what comes next. With ...