| Deep reinforcement learning approaches for process control SPK Spielberg, RB Gopaluni, PD Loewen 2017 6th International Symposium on Advanced Control of Industrial Processes …, 2017 | 184 | 2017 |
| Toward self‐driving processes: A deep reinforcement learning approach to control S Spielberg, A Tulsyan, NP Lawrence, PD Loewen, R Bhushan Gopaluni AIChE journal 65 (10), e16689, 2019 | 147 | 2019 |
| A deep learning architecture for predictive control SSP Kumar, A Tulsyan, B Gopaluni, P Loewen IFAC-PapersOnLine 51 (18), 512-517, 2018 | 91 | 2018 |
| Deep reinforcement learning for process control: A primer for beginners S Spielberg, A Tulsyan, NP Lawrence, PD Loewen, RB Gopaluni arXiv preprint arXiv:2004.05490, 2020 | 36 | 2020 |
| Offset-free model predictive control with explicit performance specification M Wallace, SS Pon Kumar, P Mhaskar Industrial & Engineering Chemistry Research 55 (4), 995-1003, 2016 | 27 | 2016 |
| FactStore: A distributed data version-control and collaborative system for Data Centric AI development S Spielberg P, MM Rebecca Boyle AMLC 2022, 2022 | | 2022 |
| Deep Reinforcement Learning for Model Predictive Control SSP Bhushan Gopaluni, Siang Lim AIChE Spring Meeting and Global Congress on Process Safety, Orlando, USA, 2018, 2018 | | 2018 |
| Recurrent Neural Networks II PK Steven Spielberg, K Shen https://www.stevenspielberg.me/projects/docs/rnn_fall2016.pdf, 2016 | | 2016 |
| GAUSSIAN COPULA MODELS SS PK, K Shen https://www.cs.ubc.ca/labs/lci/mlrg/slides/GaussianCopula_GraphicalModels.pdf, 2016 | | 2016 |
| Reinforcement Learning with Functional Approximation Using a Neural Network SS P, S Sarkaria | | 2015 |
