Researchers at Skoltech have proposed a new approach to training neural networks for wave propagation in absorbing media. The ...

Physics-informed neural networks (PINNs) have shown remarkable prospects in solving forward and inverse problems involving ...

Researchers have demonstrated a new training technique that significantly improves the accuracy of graph neural networks (GNNs)—AI systems used in applications from drug discovery to weather ...

The multiple condition (MC)-retention model is an uncertainty-aware graph-based neural network that predicts liquid chromatography (LC) retention times across multiple column chem ...

Researchers have proposed a Fourier graph neural network for estimating the state of health of lithium-ion batteries while ...

Intel TSNC brings neural texture compression with up to 18x reduction, faster decoding, and flexible SDK support for modern ...

This article discusses legal issues surrounding the harms caused by artificial intelligence, and where liability may lie if a neural network is not functioning correctly in the real world.Artificial ...

This valuable study presents a plastic recurrent spiking network model that spontaneously generates repeating neuronal sequences under unstructured inputs. The authors provide solid evidence that, ...

MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, released a forward-looking technological achievement: the hybrid quantum-classical three-dimensional ...

Neural-network processors accelerate AI program execution while development tools help you get to market fast.

Among the many steps along the road to high-performance AI, one of the most important was taken in 2007 by Fei-Fei Li, then an assistant professor in Princeton’s computer science department. Using ...