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 ...
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 ...
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 ...
FLAMeS, a new convolutional neural network, enhances MS lesion segmentation accuracy using only T2-weighted FLAIR images, ...
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Overparameterized neural networks: Feature learning precedes overfitting, research finds
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 ...
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 ...
Binary digits and circuit patterns forming a silhouette of a head. Neural networks and deep learning are closely related artificial intelligence technologies. While they are often used in tandem, ...
A new fingerprint matching algorithm developed by Precise Biometrics delivers significantly higher accuracy, along with stronger security and easier use.
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