Review of Deep Learning
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Abstract (arXiv)
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 speech processing, computer vision, natural language processing and so on. Finally, this paper discusses the existing problems of deep learning and gives the corresponding possible solutions.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Apr 2018.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 0; 2025: 1.
5 papers.
- 2021Stochastic Physics-Informed Neural Ordinary Differential Equationscited by 19
- 2022A Novel Anomaly Detection Method for Multimodal WSN Data Flow via a Dynamic Graph Neural Networkcited by 3
- 2025PolypFlow: Reinforcing Polyp Segmentation with Flow-Driven Dynamicscited by 2
- 2023BugNIST -- a Large Volumetric Dataset for Object Detection under Domain Shiftcited by 1
- 2022A Novel Self-Supervised Learning-Based Anomaly Node Detection Method Based on an Autoencoder in Wireless Sensor Networkscited by 0
- 2025PolypFlow: Reinforcing Polyp Segmentation with Flow-Driven Dynamics26 Feb 2025
- 2023BugNIST -- a Large Volumetric Dataset for Object Detection under Domain Shift4 Apr 2023
- 2022A Novel Self-Supervised Learning-Based Anomaly Node Detection Method Based on an Autoencoder in Wireless Sensor Networks26 Dec 2022
- 2022A Novel Anomaly Detection Method for Multimodal WSN Data Flow via a Dynamic Graph Neural Network19 Feb 2022
- 2021Stochastic Physics-Informed Neural Ordinary Differential Equations3 Sep 2021