Research Papers On Artificial Neural Networks

Research Papers On Artificial Neural Networks-36
Tatalović was at the time exploring AI in science journalism as his Knight fellowship project."And so we tried a few natural language processing tasks on it," Soljačić says.

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7 percent of surveyed individuals tested positive for raccoon roundworm antibodies.Such systems are widely used for pattern recognition, such as learning to identify objects depicted in photos.But neural networks in general have difficulty correlating information from a long string of data, such as is required in interpreting a research paper.The key concept is something they call a rotational unit of memory (RUM).Essentially, the system represents each word in the text by a vector in multidimensional space -- a line of a certain length pointing in a particular direction.Various tricks have been used to improve this capability, including techniques known as long short-term memory (LSTM) and gated recurrent units (GRU), but these still fall well short of what's needed for real natural-language processing, the researchers say.The team came up with an alternative system, which instead of being based on the multiplication of matrices, as most conventional neural networks are, is based on vectors rotating in a multidimensional space.Over 90 percent of raccoons in Santa Barbara play host to this parasite.Already, the RUM-based system has been expanded so it can "read" through entire research papers, not just the abstracts, to produce a summary of their contents.The researchers have even tried using the system on their own research paper describing these findings -- the paper that this news story is attempting to summarize.Here is the new neural network's summary: Researchers have developed a new representation process on the rotational unit of RUM, a recurrent memory that can be used to solve a broad spectrum of the neural revolution in natural language processing.


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