| The multimodal brain tumor image segmentation benchmark (BRATS) BH Menze, A Jakab, S Bauer, J Kalpathy-Cramer, K Farahani, J Kirby, ... IEEE transactions on medical imaging 34 (10), 1993-2024, 2014 | 6547 | 2014 |
| Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning RS Sutton, D Precup, S Singh Artificial intelligence 112 (1-2), 181-211, 1999 | 4716 | 1999 |
| Deep reinforcement learning that matters P Henderson, R Islam, P Bachman, J Pineau, D Precup, D Meger Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 2598 | 2018 |
| Off-policy deep reinforcement learning without exploration S Fujimoto, D Meger, D Precup International conference on machine learning, 2052-2062, 2019 | 1792 | 2019 |
| The option-critic architecture PL Bacon, J Harb, D Precup Proceedings of the AAAI conference on artificial intelligence 31 (1), 2017 | 1355 | 2017 |
| Eligibility traces for off-policy policy evaluation D Precup Computer Science Department Faculty Publication Series, 80, 2000 | 1014 | 2000 |
| Fast gradient-descent methods for temporal-difference learning with linear function approximation RS Sutton, HR Maei, D Precup, S Bhatnagar, D Silver, C Szepesvári, ... Proceedings of the 26th annual international conference on machine learning …, 2009 | 749 | 2009 |
| Learning with pseudo-ensembles P Bachman, O Alsharif, D Precup Advances in neural information processing systems 27, 2014 | 736 | 2014 |
| Reward is enough D Silver, S Singh, D Precup, RS Sutton Artificial Intelligence 299, 103535, 2021 | 666 | 2021 |
| Horde: A scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction RS Sutton, J Modayil, M Delp, T Degris, PM Pilarski, A White, D Precup The 10th International Conference on Autonomous Agents and Multiagent …, 2011 | 640 | 2011 |
| Algorithms for multi-armed bandit problems V Kuleshov, D Precup arXiv preprint arXiv:1402.6028, 2014 | 614 | 2014 |
| Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation T Nair, D Precup, DL Arnold, T Arbel Medical image analysis 59, 101557, 2020 | 514 | 2020 |
| Learning options in reinforcement learning M Stolle, D Precup Abstraction, Reformulation, and Approximation: 5th International Symposium …, 2002 | 496 | 2002 |
| Off-policy temporal-difference learning with function approximation D Precup, RS Sutton, S Dasgupta ICML, 417-424, 2001 | 479 | 2001 |
| Temporal abstraction in reinforcement learning D Precup University of Massachusetts Amherst, 2000 | 418 | 2000 |
| Metrics for Finite Markov Decision Processes. N Ferns, P Panangaden, D Precup UAI 4, 162-169, 2004 | 382 | 2004 |
| Conditional computation in neural networks for faster models E Bengio, PL Bacon, J Pineau, D Precup arXiv preprint arXiv:1511.06297, 2015 | 375 | 2015 |
| Convergent temporal-difference learning with arbitrary smooth function approximation H Maei, C Szepesvari, S Bhatnagar, D Precup, D Silver, RS Sutton Advances in neural information processing systems 22, 2009 | 362 | 2009 |
| Deep learning, reinforcement learning, and world models Y Matsuo, Y LeCun, M Sahani, D Precup, D Silver, M Sugiyama, E Uchibe, ... Neural Networks 152, 267-275, 2022 | 361 | 2022 |
| Reproducibility of benchmarked deep reinforcement learning tasks for continuous control R Islam, P Henderson, M Gomrokchi, D Precup arXiv preprint arXiv:1708.04133, 2017 | 342 | 2017 |
Joelle PineauSchool of Computer Science, McGill University; FAIR, Meta AI; Milacs.mcgill.ca의 이메일 확인됨
