Seguir
Doina Precup
Doina Precup
DeepMind and McGill University
Dirección de correo verificada de cs.mcgill.ca
Título
Citado por
Citado por
Año
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
53272014
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
42691999
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
22072018
Off-policy deep reinforcement learning without exploration
S Fujimoto, D Meger, D Precup
International conference on machine learning, 2052-2062, 2019
13472019
The option-critic architecture
PL Bacon, J Harb, D Precup
Proceedings of the AAAI conference on artificial intelligence 31 (1), 2017
11672017
Eligibility traces for off-policy policy evaluation
D Precup
Computer Science Department Faculty Publication Series, 80, 2000
9132000
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
6942009
Learning with pseudo-ensembles
P Bachman, O Alsharif, D Precup
Advances in neural information processing systems 27, 2014
6192014
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
5702011
Algorithms for multi-armed bandit problems
V Kuleshov, D Precup
arXiv preprint arXiv:1402.6028, 2014
5292014
Reward is enough
D Silver, S Singh, D Precup, RS Sutton
Artificial Intelligence 299, 103535, 2021
5022021
Off-policy temporal-difference learning with function approximation
D Precup, RS Sutton, S Dasgupta
ICML, 417-424, 2001
4542001
Learning options in reinforcement learning
M Stolle, D Precup
Abstraction, Reformulation, and Approximation: 5th International Symposium …, 2002
4502002
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
4422020
Temporal abstraction in reinforcement learning
D Precup
University of Massachusetts Amherst, 2000
3862000
Metrics for Finite Markov Decision Processes.
N Ferns, P Panangaden, D Precup
UAI 4, 162-169, 2004
3342004
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
3272009
Conditional computation in neural networks for faster models
E Bengio, PL Bacon, J Pineau, D Precup
arXiv preprint arXiv:1511.06297, 2015
3212015
Reproducibility of benchmarked deep reinforcement learning tasks for continuous control
R Islam, P Henderson, M Gomrokchi, D Precup
arXiv preprint arXiv:1708.04133, 2017
2992017
Gradient starvation: A learning proclivity in neural networks
M Pezeshki, O Kaba, Y Bengio, AC Courville, D Precup, G Lajoie
Advances in Neural Information Processing Systems 34, 1256-1272, 2021
2322021
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
Artículos 1–20