Deep learning is increasingly used in financial modeling, but its lack of transparency raises risks. Using the well-known Heston option pricing model as a benchmark, researchers show that global ...

As sensor data overwhelms the cloud, Innatera’s neuromorphic chips bring always-on, ultra-low-power AI directly to the edge. But how?

A new study published in Translational Psychiatry has found that post-traumatic stress disorder is associated with ...

(Boston)—Recently, there has been convergence of thought by researchers in the fields of memory, perception, and neurology that the same neural circuitry that produces conscious memory of the past not ...

During a Wednesday interview on CNN’s "The Lead with Jake Tapper," Rep. Ilhan Omar, D-Minn., appeared to struggle to explain how fraud became "so out of control" in Minnesota after federal officials ...

Abstract: Convolutional Neural Networks (CNNs) are one of the most important and successful algorithmic architectures in deep learning, especially effectively in processing data with a grid-like ...

Machine learning models called convolutional neural networks (CNNs) power technologies like image recognition and language translation. A quantum counterpart—known as a quantum convolutional neural ...

Explore Highway Networks, a neural network architecture designed to improve training of deep networks. Concepts and examples explained. #HighwayNetworks #DeepLearning #NeuralNetworks Tropical Storm ...

Abstract: This study investigates the application of Spiking Neural Network (SNN) in seismic signal denoising by developing a Convolutional Neural Network (CNN) to SNN conversion framework. We focus ...

This academic seminar series explores a broad range of topics such as data science, urban planning and urban networks and ...