mlsanityData to 7 Oct 2026

Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey

arXiv 2209.11123 ↗First posted 22 Sep 2022cs.LG cs.SY eess.SYcited by 6

Not on the map. Why?

Abstract (arXiv)

Over the last ten years, we have seen a significant increase in industrial data, tremendous improvement in computational power, and major theoretical advances in machine learning. This opens up an opportunity to use modern machine learning tools on large-scale nonlinear monitoring and control problems. This article provides a survey of recent results with applications in the process industry.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 25 of its 167 references that are in the map. First posted on arXiv 22 Sep 2022.

Builds on

25 of 167 references.

References · 25

  1. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  3. 2013Auto-Encoding Variational Bayescited by 3,980
  4. 2015Continuous control with deep reinforcement learningcited by 3,279
  5. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
  6. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,179
  7. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,084
  8. 2018On First-Order Meta-Learning Algorithmscited by 1,003
  9. 2015End-to-End Training of Deep Visuomotor Policiescited by 847
  10. 2019When to Trust Your Model: Model-Based Policy Optimizationcited by 753
  11. 2017Deep Reinforcement Learning that Matterscited by 727
  12. 2016Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learningcited by 209
13 more
  1. 2018Learning to Walk via Deep Reinforcement Learningcited by 153
  2. 2018A Tour of Reinforcement Learning: The View from Continuous Controlcited by 129
  3. 2016Invertible Conditional GANs for image editingcited by 118
  4. 2019Quant GANs: Deep Generation of Financial Time Seriescited by 107
  5. 2018GANs for Medical Image Analysiscited by 81
  6. 2018Anomaly Detection with Generative Adversarial Networks for Multivariate Time Seriescited by 55
  7. 2018Global Deterministic Optimization with Artificial Neural Networks Embeddedcited by 32
  8. 2017The ALAMO approach to machine learningcited by 18
  9. 2020Deep Reinforcement Learning for Process Control: A Primer for Beginnerscited by 15
  10. 2019LSTM Neural Networks: Input to State Stability and Probabilistic Safety Verificationcited by 12
  11. 2019Task-Agnostic Dynamics Priors for Deep Reinforcement Learningcited by 7
  12. 2014Computer vision-based recognition of liquid surfaces and phase boundaries in transparent vessels, with emphasis on chemistry applicationscited by 4
  13. 2018Classification of crystallization outcomes using deep convolutional neural networkscited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 1.

6 papers.

  1. 2024Application-Driven Innovation in Machine Learningcited by 24
  2. 2024Machine learning for industrial sensing and control: A survey and practical perspectivecited by 6
  3. 2025Sample-Efficient Reinforcement Learning of Koopman eNMPCcited by 1
  4. 2025Precise Insulin Delivery for Artificial Pancreas: A Reinforcement Learning Optimized Adaptive Fuzzy Control Approachcited by 0
  5. 2025Computationally efficient Gauss-Newton reinforcement learning for model predictive controlcited by 0
  6. 2026Optimizing Task Completion Time Updates Using POMDPscited by 0