What can you do about data sparsity? What do you do when you have a matrix with a bunch of zeros in it, and you can't get a good look at a complex system because so many of the nodes are empty? Matrix ...

A machine-learning approach developed for sparse data reliably predicts fault slip in laboratory earthquakes and could be key to predicting fault slip and potentially earthquakes in the field. A ...

AI is rapidly being adopted in the pharmaceutical industry, particularly for improving predictive models in drug discovery and early preclinical development. Fueled by the large amounts of data ...

This book offers a comprehensive framework for mastering the complexities of learning high-dimensional sparse graphical models through the use of conditional independence tests. These tests are ...

An innovative approach to artificial intelligence (AI) enables reconstructing a broad field of data, such as overall ocean temperature, from a small number of field-deployable sensors using ...

Massachusetts for the first time Wednesday made public racial and ethnic information about the victims of the new coronavirus, but data was so incomplete it provided little insight into the pandemic’s ...

AI is rapidly being adopted in the pharmaceutical industry, particularly for improving predictive models in drug discovery and early preclinical development. Fueled by the large amounts of data ...