A neural network has discovered over 800 anomalies in old Hubble images, primarily unusual galaxies. Dozens of findings defy ...

AI is ultimately a story about selfhood—and the answer will not be found in the machine, but in what mindful awareness allows ...

Tesla Full Self-Driving leverages cameras, neural networks, and real-world testing to navigate traffic safely, advancing ...

Evolving challenges and strategies in AI/ML model deployment and hardware optimization have a big impact on NPU architectures ...

A new technical paper titled “A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware” was published by ...

Researchers have developed a machine learning model capable of predicting whether a patient with depression will respond to ...

In a Nature Communications study, researchers from China have developed an error-aware probabilistic update (EaPU) method ...

It shows the schematic of the physics-informed neural network algorithm for pricing European options under the Heston model. The market price of risk is taken to be λ=0. Automatic differentiation is ...

The relentless advancement of artificial intelligence (AI) across sectors such as healthcare, the automotive industry, and social media necessitates the development of more efficient hardware ...

TPUs are Google’s specialized ASICs built exclusively for accelerating tensor-heavy matrix multiplication used in deep learning models. TPUs use vast parallelism and matrix multiply units (MXUs) to ...

When Fei-Fei Li arrived in Princeton in January 2007 as an assistant professor, she was assigned an office on the second floor of the computer science building. Her neighbor was Christiane Fellbaum.

Abstract: Neural networks (NNs) are pivotal in enhancing data processing tasks such as classification, generation, and restoration. A crucial consideration in these applications is the signal-to-noise ...