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
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Image Classification | CIFAR-100 (test) | -- | 3518 | |
| Node Classification | Chameleon | Accuracy46.21 | 936 | |
| Image Classification | CIFAR-10 (test) | -- | 906 | |
| Node Classification | Cornell | Accuracy81.89 | 900 | |
| Node Classification | Wisconsin | Accuracy85.29 | 898 | |
| Node Classification | Texas | -- | 859 | |
| Object Detection | PASCAL VOC 2007 (test) | -- | 844 | |
| Image Classification | MNIST (test) | Accuracy98 | 654 | |
| Node Classification | Pubmed | Accuracy75.69 | 627 | |
| Node Classification | Cora | Accuracy87.16 | 609 |
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