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 ...
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 ...
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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, ...
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 ...
The work that we’re doing brings AI closer to human thinking,” said Mick Bonner, who teaches cognitive science at Hopkins.
Neuromorphic computing systems, encompassing both digital and analog neural accelerators, promise to revolutionize AI ...
Layered metasurfaces trained as optical neural networks enable multifunctional holograms and security features, integrating ...
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