Review of Deep Learning

By Rong Zhang, Weiping Li, Tong Mo. 2018

167 citations recorded by the source corpus.

In recent years, China, the United States and other countries, Google and other high-tech companies have increased investment in artificial intelligence. Deep learning is one of the current artificial intelligence research's key areas. This paper analyzes and summarizes the latest progress and future research directions of deep learning. Firstly, three basic models of deep learning are outlined, including multilayer perceptrons, convolutional neural networks, and recurrent neural networks. On this basis, we further analyze the emerging new models of convolution neural networks and recurrent neural networks. This paper then summarizes deep learning's applications in many areas of artificial intelligence, including voice, computer vision, natural language processing and so on. Finally, this paper discusses the existing problems of deep learning and gives the corresponding possible solutions.

Subfields: Artificial Intelligence

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Dan Boneh
2. Florian Tramèr
3. Giancarlo Pellegrino
4. Lin Wu
5. Fuan Wang
6. Huiwei Zhao
7. Edward Chou
8. Ying Liu

Citations in the corpus

• SentiNet: Detecting Localized Universal Attacks Against Deep Learning Systems (2018, background): “ssification task such as a face-based user authentication mechanisms, voice recognition, or voice-to-text system. Recently, DL systems have been proposed for autonomous vehicles and digital assistants [48]. In this paper, we focus on DL systems acting while processing physical scenes, where data is a”
• SentiNet: Detecting Physical Attacks Against Deep Learning Systems (2018, background): “Recently, DL systems have been proposed for autonomous vehicles and digital assistants [48].”
• Geological Disaster Recognition on Optical Remote Sensing Images Using Deep Learning (2016, background): “Deep learning is a hot topic in machine learning and artificial intelligence, which imitates the mechanism of human brain in interpreting data [7].”
• An enhancement deep feature fusion method for rotating machinery fault diagnosis (2017, background): “The most attractive advantage of deep learning methods is that they can adaptively learn useful features from the raw data through multiple nonlinear transformations [17].”