Neural networks are distributed computing structures inspired by the structure of a biological brain and aim to achieve cognitive performance comparable to that of humans but in a much shorter time. These technologies now form the basis of machine learning and artificial intelligence systems that can perceive the environment and adapt their own behaviour by analysing the effects of previous actions and working autonomously.
Tag:
neural networks
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An international team led by scientists at the University of Sydney has demonstrated nanowire networks can exhibit both short- and long-term memory like the human brain. The research has been published in the journal Science Advances, led by Dr Alon Loeffler, who received his PhD in the School of Physics, with collaborators in Japan.
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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.
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Funded by the National Institute for Transportation and Communities, the latest Small Starts project led by Abbas Rashidi of the University of Utah introduces a robust, deep neural network model for analyzing the automobile traffic impacts of construction zones.
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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.
