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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
Object DetectionPASCAL VOC 2007 (test)--
844
Image ClassificationCIFAR10 (test)
Accuracy96.53
585
Fine-grained Image ClassificationCUB200 2011 (test)
Accuracy86.2
543
Image ClassificationCIFAR100 (test)
Top-1 Accuracy79.35
407
Node ClassificationChameleon (test)
Mean Accuracy46.21
297
Node ClassificationCornell (test)
Mean Accuracy81.89
274
Node ClassificationTexas (test)
Mean Accuracy80.81
269
Node ClassificationSquirrel (test)
Mean Accuracy28.77
267
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