Researchers have developed an algorithm to train an analog neural network just as accurately as a digital one, enabling the development of more efficient alternatives to power-hungry deep learning ...

EPFL researchers have developed an algorithm to train an analog neural network just as accurately as a digital one, enabling the development of more efficient alternatives to power-hungry deep ...

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 ...

For about a decade, computer engineer Kerem Çamsari employed a novel approach known as probabilistic computing. Based on probabilistic bits (p-bits), it’s used to solve an array of complex ...

Deep neural networks (DNNs), which power modern artificial intelligence (AI) models, are machine learning systems that learn hidden patterns from various types of data, be it images, audio or text, to ...

Modern neural networks, with billions of parameters, are so overparameterized that they can "overfit" even random, ...

MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, innovatively launches a quantum-enhanced deep convolutional neural network image 3D reconstruction ...

FLAMeS, a new convolutional neural network, enhances MS lesion segmentation accuracy using only T2-weighted FLAIR images, ...

Scientists in Spain have used genetic algorithms to optimize a feedforward artificial neural network for the prediction of energy generation of PV systems. Genetic algorithms use “parents” and ...