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 | 5687 | 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 | 4402 | 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 | 2324 | 2018 |
Off-policy deep reinforcement learning without exploration S Fujimoto, D Meger, D Precup International conference on machine learning, 2052-2062, 2019 | 1491 | 2019 |
The option-critic architecture PL Bacon, J Harb, D Precup Proceedings of the AAAI conference on artificial intelligence 31 (1), 2017 | 1230 | 2017 |
Eligibility traces for off-policy policy evaluation D Precup Computer Science Department Faculty Publication Series, 80, 2000 | 942 | 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 | 713 | 2009 |
Learning with pseudo-ensembles P Bachman, O Alsharif, D Precup Advances in neural information processing systems 27, 2014 | 653 | 2014 |
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 | 597 | 2011 |
Algorithms for multi-armed bandit problems V Kuleshov, D Precup arXiv preprint arXiv:1402.6028, 2014 | 551 | 2014 |
Reward is enough D Silver, S Singh, D Precup, RS Sutton Artificial Intelligence 299, 103535, 2021 | 540 | 2021 |
Off-policy temporal-difference learning with function approximation D Precup, RS Sutton, S Dasgupta ICML, 417-424, 2001 | 464 | 2001 |
Learning options in reinforcement learning M Stolle, D Precup Abstraction, Reformulation, and Approximation: 5th International Symposium …, 2002 | 461 | 2002 |
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 | 455 | 2020 |
Temporal abstraction in reinforcement learning D Precup University of Massachusetts Amherst, 2000 | 394 | 2000 |
Metrics for Finite Markov Decision Processes. N Ferns, P Panangaden, D Precup UAI 4, 162-169, 2004 | 346 | 2004 |
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 | 340 | 2009 |
Conditional computation in neural networks for faster models E Bengio, PL Bacon, J Pineau, D Precup arXiv preprint arXiv:1511.06297, 2015 | 336 | 2015 |
Reproducibility of benchmarked deep reinforcement learning tasks for continuous control R Islam, P Henderson, M Gomrokchi, D Precup arXiv preprint arXiv:1708.04133, 2017 | 317 | 2017 |
Towards continual reinforcement learning: A review and perspectives K Khetarpal, M Riemer, I Rish, D Precup Journal of Artificial Intelligence Research 75, 1401-1476, 2022 | 252 | 2022 |