Researchers from the University of California, Los Angeles (UCLA) have developed a chemical imaging system that combines high-performance terahertz time-domain spectroscopy with advanced deep learning ...
Deep learning is increasingly used in financial modeling, but its lack of transparency raises risks. Using the well-known ...
Abstract: To apply convolutional neural networks (CNNs) on high-resolution images, a common approach is to split the input image into smaller patches. However, the field-of-view is restricted by the ...
Thread is a protocol designed to connect smart home devices in a wireless mesh network. It works much like Wi-Fi but requires less power. With Thread, devices from any manufacturer can create a ...
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Highway Networks – Deep Neural Network Explained
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 their impact on the dynamic of cities.
A newly developed silicon photonic chip turns light-encoded data into instant convolution results. Credit: H. Yang (University of Florida). Artificial intelligence has become a central part of modern ...
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