A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends

By William Grant Hatcher, Wei Yu. IEEE Access, 2018

533 citations recorded by the source corpus.

Deep learning has exploded in the public consciousness, primarily as predictive and analytical products suffuse our world, in the form of numerous human-centered smart-world systems, including targeted advertisements, natural language assistants and interpreters, and prototype self-driving vehicle systems. Yet to most, the underlying mechanisms that enable such human-centered smart products remain obscure. In contrast, researchers across disciplines have been incorporating deep learning into their research to solve problems that could not have been approached before. In this paper, we seek to provide a thorough investigation of deep learning in its applications and mechanisms. Specifically, as a categorical collection of state of the art in deep learning research, we hope to provide a broad reference for those seeking a primer on deep learning and its various implementations, platforms, algorithms, and uses in a variety of smart-world systems. Furthermore, we hope to outline recent key advancements in the technology, and provide insight into areas, in which deep learning can improve investigation, as well as highlight new areas of research that have yet to see the application of deep learning, but could nonetheless benefit immensely. We hope this survey provides a valuable reference for new deep learning practitioners, as well as those seeking to innovate in the application of deep learning.

Subfields: Computer Networks and Communications, Computer Science Applications, Transportation

View Paper: https://www.semanticscholar.org/paper/654892e454d37aa64ed5f7428dc728a8075e8d31

How researchers see this paper

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

In Computer Networks and Communications

1. Weiwei Jiang
2. Xiao Wang
3. Peng Cui
4. Chuan Shi
5. Wei Yu
6. Mugen Peng
7. Vittorio Rampa
8. Monica Nicoli

In Computer Science Applications

1. Weiwei Jiang
2. Xiao Wang

In Transportation

1. Weiwei Jiang
2. Peng Cui
3. Chuan Shi
4. Xiao Wang
5. Deyu Bo

Citations in the corpus

• Structural Deep Clustering Network (2020, background): “Therefore, the deep clustering has caught significant attention [6].”
• Federated Learning With Cooperating Devices: A Consensus Approach for Massive IoT Networks (2019, background): “services [2] in several domains, such as Industry 4.”
• Toward Edge Intelligence: Multiaccess Edge Computing for 5G and Internet of Things (2020, background): “Though the fast-developing AI technologies, such as deep learning [151]–[154] and reinforcement learning [155]–[157], can help solve the complex edge intelligence problem by learning from a large amount of historical data and react efficiently to the dynamic scenarios, how to apply AI to make…”
• State-of-the-art in artificial neural network applications: A survey (2018, methodology): “The DL techniques require raw image data only [109, 110, 111, 112].”
• Applications of deep learning in stock market prediction: recent progress (2020, background): “For further reference, the readers may refer to Hatcher & Yu (2018) for a comprehensive introduction of deep learning tools.”
• A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective (2018, background): “Machine learning has demonstrated great use in leveraging algorithms and mathematical models to gain the insight and intelligence of numerous systems, including image and video recognition, natural language and text analysis, robotics, autonomous vehicles, and others [134]–[140].”
• The Deep Learning Compiler: A Comprehensive Survey (2020, background): “CNTK is designed to be easy-to-use and production-ready for large-scale data in production [37].”