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      Machine learning and the physical sciences

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          Most cited references 294

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          Deep learning.

          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
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            Long Short-Term Memory

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              Compressed sensing

               D.L. Donoho (2006)
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                Author and article information

                Journal
                RMPHAT
                Reviews of Modern Physics
                Rev. Mod. Phys.
                American Physical Society (APS)
                0034-6861
                1539-0756
                December 2019
                December 6 2019
                : 91
                : 4
                Article
                10.1103/RevModPhys.91.045002
                © 2019

                https://link.aps.org/licenses/aps-default-license

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