| Intelligent laser welding through representation, prediction, and control learning: An architecture with deep neural networks and reinforcement learning J Günther, PM Pilarski, G Helfrich, H Shen, K Diepold Mechatronics 34, 1-11, 2016 | 160 | 2016 |
| First steps towards an intelligent laser welding architecture using deep neural networks and reinforcement learning J Günther, PM Pilarski, G Helfrich, H Shen, K Diepold Procedia Technology 15, 474-483, 2014 | 102 | 2014 |
| Interpretable PID parameter tuning for control engineering using general dynamic neural networks: An extensive comparison J Günther, E Reichensdörfer, PM Pilarski, K Diepold PLOS ONE 15(12), 2020 | 16* | 2020 |
| Detecting the onset of machine failure using anomaly detection methods M Riazi, O Zaiane, T Takeuchi, A Maltais, J Günther, M Lipsett Big Data Analytics and Knowledge Discovery: 21st International Conference …, 2019 | 12 | 2019 |
| Gamma-nets: Generalizing value estimation over timescale C Sherstan, S Dohare, J MacGlashan, J Günther, PM Pilarski Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5717-5725, 2020 | 9 | 2020 |
| Machine intelligence for adaptable closed loop and open loop production engineering systems J Günther Technische Universität München, 2018 | 9 | 2018 |
| Predictions, Surprise, and Predictions of Surprise in General Value Function Architectures J Günther, A Kearney, MR Dawson, C Sherstan, PM Pilarski AAAI 2018 Fall Symposium on Reasoning and Learning in Real-World Systems for …, 2018 | 9 | 2018 |
| Recurrent neural networks for PID auto-tuning E Reichensdörfer, J Günther, K Diepold | 9 | 2017 |
| Examining the use of temporal-difference incremental delta-bar-delta for real-world predictive knowledge architectures J Günther, NM Ady, A Kearney, MR Dawson, PM Pilarski Frontiers in Robotics and AI 7, 34, 2020 | 7 | 2020 |
| Affordance as general value function: A computational model D Graves, J Günther, J Luo Adaptive Behavior, 21, 2020 | 4 | 2020 |
| Meta-learning for Predictive Knowledge Architectures: A Case Study Using TIDBD on a Sensor-rich Robotic Arm J Günther, A Kearney, NM Ady, MR Dawson, PM Pilarski Proceedings of the 18th International Conference on Autonomous Agents and …, 2019 | 4 | 2019 |
| Neural Networks for fast sensor data processing in Laser Welding J Günther, H Shen, K Diepold Jahreskolloquium-Bildverarbeitung in der Automation, 2014 | 3 | 2014 |
| Finding useful predictions by meta-gradient descent to improve decision-making A Kearney, A Koop, J Günther, PM Pilarski arXiv preprint arXiv:2111.11212, 2021 | 2 | 2021 |
| Automated optimization of dynamic neural network structure using genetic algorithms C Sandner, J Günther, K Diepold | 2 | 2017 |
| What Should I Know? Using Meta-Gradient Descent for Predictive Feature Discovery in a Single Stream of Experience A Kearney, A Koop, J Günther, PM Pilarski Conference on Lifelong Learning Agents, 604-616, 2022 | 1 | 2022 |
| Stacked denoising and stacked convolutional autoencoders U Schmid, J Günther, K Diepold | 1 | 2017 |
| Five Properties of Specific Curiosity You Didn't Know Curious Machines Should Have NM Ady, R Shariff, J Günther, PM Pilarski arXiv preprint arXiv:2212.00187, 2022 | | 2022 |
| Composite Recurrent Convolutional Neural Networks Offer a Position-Aware Prosthesis Control Alternative While Balancing Predictive Accuracy with Training Burden HE Williams, J Günther, JS Hebert, PM Pilarski, AW Shehata 2022 International Conference on Rehabilitation Robotics (ICORR), 1-6, 2022 | | 2022 |
| Prototyping three key properties of specific curiosity in computational reinforcement learning NM Ady, R Shariff, J Günther, PM Pilarski arXiv preprint arXiv:2205.10407, 2022 | | 2022 |
| Prediction, Knowledge, and Explainability: Examining the Use of General Value Functions in Machine Knowledge A Kearney, J Günther, PM Pilarski Frontiers in Artificial Intelligence 5, 2022 | | 2022 |
Patrick M. PilarskiAmii and University of AlbertaVerified email at ualberta.ca
