Machine learning: Overview of the recent progresses and implications for the process systems engineering field
By Jay H. Lee, Joohyun Shin, M. Realff. Computers and Chemical Engineering, 2017
352 citations recorded by the source corpus.
Abstract Machine learning (ML) has recently gained in popularity, spurred by well-publicized advances like deep learning and widespread commercial interest in big data analytics. Despite the enthusiasm, some renowned experts of the field have expressed skepticism, which is justifiable given the disappointment with the previous wave of neural networks and other AI techniques. On the other hand, new fundamental advances like the ability to train neural networks with a large number of layers for hierarchical feature learning may present significant new technological and commercial opportunities. This paper critically examines the main advances in deep learning. In addition, connections with another ML branch of reinforcement learning are elucidated and its role in control and decision problems is discussed. Implications of these advances for the fields of process and energy systems engineering are also discussed.
Subfields: Control and Systems Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/38586876c01c08fd90b2b4ed7bcf0359d4257b19
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In Control and Systems Engineering
In Electrical and Electronic Engineering
Citations in the corpus
- Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming (2019, background): “For extensive surveys on deep learning in the PSE area, we refer the reader to the review papers on this subject [14, 197].”