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Deep Learning

About

Deep learning (DL) is a high dimensional data reduction technique for constructing high-dimensional predictors in input-output models. DL is a form of machine learning that uses hierarchical layers of latent features. In this article, we review the state-of-the-art of deep learning from a modeling and algorithmic perspective. We provide a list of successful areas of applications in Artificial Intelligence (AI), Image Processing, Robotics and Automation. Deep learning is predictive in its nature rather then inferential and can be viewed as a black-box methodology for high-dimensional function estimation.

Nicholas G. Polson, Vadim O. Sokolov• 2018

Related benchmarks

TaskDatasetResultRank
Image ClassificationCIFAR-100 (test)--
3518
Image ClassificationCIFAR-10 (test)--
906
Node ClassificationChameleon
Accuracy46.21
867
Node ClassificationWisconsin
Accuracy85.29
864
Node ClassificationCornell
Accuracy81.89
851
Object DetectionPASCAL VOC 2007 (test)--
844
Node ClassificationTexas--
801
Node ClassificationPubmed
Accuracy75.69
627
Image ClassificationCIFAR10 (test)
Accuracy96.53
585
Node ClassificationCora
Accuracy87.16
583
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