A survey of deep neural network architectures and their applications

By Weibo Liu, Zidong Wang, Xiaohui Liu, Nianyin Zeng, Yurong Liu, Fuad E. Alsaadi. 2017

3,019 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Model Architectures

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Citations in the corpus

A survey of data fusion in smart city applications (2019, methodology): “In this context, we discuss two research trends on applying machine learning techniques in data fusion as follows: 1) Explainable Deep Neural Network: Lately, supervised machine learning techniques focus on the DNN, where the in-depth reviews of the recent development can be found in [255]–[257].”
Survey on Automated Machine Learning (2019, background): “an ML pipeline is creating the pipeline structure. This topic has received a lot of attention in the context of designing neural networks referred to as architecture search, e.g., (Zoph and Le, 2017; Liu et al., 2017). Regarding classical machine learning, this topic was tackled for various specic p”
Reinforcement Learning in Healthcare: A Survey (2019, background): “into RL [47], [48], fostering a new flourishing research area of Deep RL (DRL) [12].”
Recommendation system based on deep learning methods: a systematic review and new directions (2019, methodology): “Recurrent Neural Network RNN is specifically used to model sequential data by simply incorporating temporal layers to capture sequential information (Liu et al. 2017b).”
Delphi: A Cryptographic Inference Service for Neural Networks (2020, neutral): “Recent advances in machine learning have driven increasing deployment of neural network inference in popular applications like voice assistants [3] and image classification [19].”
P2T: Pyramid Pooling Transformer for Scene Understanding (2021, background): “Please refer to [38], [39] for a more extensive survey.”
Artificial neural networks applications in economics and management research: An exploratory literature review (2018, background): “[5], deep learning techniques have drawn increasing interests in research since the introduction of a fast learning algorithm for deep belief networks in 2006.”
A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications (2020, background): “Nevertheless, for numerous tasks, it is not very easy to manually select a good group of features to be used for training the AI system [86].”
Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions (2021, methodology): “The AEs have recently been used to learn generative data models [69].”