A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends

By Saptarshi Sengupta, Sanchita Basak, Pallabi Saikia, Sayak Paul, Vasilios Tsalavoutis, Frederick Ditliac Atiah, Vadlamani Ravi, R. Peters. Knowledge-Based Systems, 2019

402 citations recorded by the source corpus.

Deep learning has taken over - both in problems beyond the realm of traditional, hand-crafted machine learning paradigms as well as in capturing the imagination of the practitioner sitting on top of petabytes of data. While the public perception about the efficacy of deep neural architectures in complex pattern recognition tasks grows, sequentially up-to-date primers on the current state of affairs must follow. In this review, we seek to present a refresher of the many different stacked, connectionist networks that make up the deep learning architectures followed by automatic architecture optimization protocols using multi-agent approaches. Further, since guaranteeing system uptime is fast becoming an indispensable asset across multiple industrial modalities, we include an investigative section on testing neural networks for fault detection and subsequent mitigation. This is followed by an exploratory survey of several application areas where deep learning has emerged as a game-changing technology - be it anomalous behavior detection in financial applications or financial time-series forecasting, predictive and prescriptive analytics, medical imaging, natural language processing or power systems research. The thrust of this review is on outlining emerging areas of application-oriented research within the deep learning community as well as to provide a handy reference to researchers seeking to embrace deep learning in their work for what it is: statistical pattern recognizers with unparalleled hierarchical structure learning capacity with the ability to scale with information.

View Paper: https://www.semanticscholar.org/paper/e6ed2eae6d810deb0dd00b2bdedf07252dedd51b

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Citations in the corpus

Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next (2022, background): “Finally, the taxonomy of various Deep Learning architectures is still a matter of research (Muhammad et al, 2021; Aldweesh et al, 2020; Sengupta et al, 2020) The main architectures covered in the Deep Learning literature include fully connected feed-forward networks (called FFNN, or also FNN, FCNN,…”
Recent Advances and Trends in Multimodal Deep Learning: A Review (2021, background): “Machines are developed to perform similarly to humans or even better in other application areas such as self-driving cars, image processing, medical diagnosis and predictive forecasting etc [129].”