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.