deep learning

Bar-Ilan University researchers have shown that brain-inspired shallow neural networks can achieve the same classification success rates as deep learning architectures consisting of many layers and filters, but with less computational complexity. The findings suggest that efficient learning of non-trivial classification tasks can be achieved using shallow feedforward networks, potentially requiring less computational complexity.

Self-driving cars, or autonomous vehicles, have long been earmarked as the next generation mode of transport. To enable the autonomous navigation of such vehicles in different environments, many different technologies relating to signal processing, image processing, artificial intelligence deep learning, edge computing, and IoT, need to be implemented.

Compared to standard machine learning models, deep learning models are largely superior at discerning patterns and discriminative features in brain imaging, despite being more complex in their architecture, according to a new study in Nature Communications led by Georgia State University.

Researchers at the George Washington University, together with researchers at the University of California, Los Angeles, and the deep-tech venture startup Optelligence LLC, have developed an optical convolutional neural network accelerator capable of processing large amounts of information, on the order of petabytes, per second.

Researchers at the University of Illinois Urbana-Champaign developed a new method that brings physics into the machine learning process to make better predictions on turbulence. Deep learning reproduces data to model problem scenarios and offer solutions. However, some problems in physics, like turbulence, are unknown or cannot be represented in detail mathematically on a computer.