Sotabase

Career

· Research Team Lead / Head of Montreal Office, Google DeepMind2017–
· Associate Professor, School of Computer Science, McGill University2000–2017
· Master of Science, University of Massachusetts Amherst1995–1997
· PhD in Computer Science, University of Massachusetts Amherst1995–2000
· Master of Science in Computer Science and Engineering, Technical University of Cluj-Napoca1994–1995
· Bachelor of Science in Computer Science and Engineering, Technical University of Cluj-Napoca1990–1994

Publications (407)

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
IEEE Transactions on Medical Imaging · 2015
5,762
cited
Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning
Artificial Intelligence · 1999
4,030
cited
Deep Reinforcement Learning that Matters
AAAI Conference on Artificial Intelligence · 2017
2,173
cited
Off-Policy Deep Reinforcement Learning without Exploration
International Conference on Machine Learning · 2018
1,876
cited
The Option-Critic Architecture
AAAI Conference on Artificial Intelligence · 2016
1,202
cited
Eligibility Traces for Off-Policy Policy Evaluation
International Conference on Machine Learning · 2000
876
cited
Fast gradient-descent methods for temporal-difference learning with linear function approximation
International Conference on Machine Learning · 2009
687
cited
Learning with Pseudo-Ensembles
Neural Information Processing Systems · 2014
645
cited
Horde: a scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction
Adaptive Agents and Multi-Agent Systems · 2011
529
cited
Reward is enough
Artificial Intelligence · 2021
508
cited
Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation
International Conference on Medical Image Computing and Computer-Assisted Intervention · 2018
479
cited
Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation
Neural Information Processing Systems · 2021
442
cited
Off-Policy Temporal Difference Learning with Function Approximation
International Conference on Machine Learning · 2001
427
cited
Deep learning, reinforcement learning, and world models
Neural Networks · 2022
415
cited
Algorithms for multi-armed bandit problems
arXiv.org · 2014
390
cited
Towards Continual Reinforcement Learning: A Review and Perspectives
Journal of Artificial Intelligence Research · 2020
385
cited
Learning Options in Reinforcement Learning
Symposium on Abstraction, Reformulation and Approximation · 2002
378
cited
Temporal abstraction in reinforcement learning
International Conference on Machine Learning · 2000
368
cited
Conditional Computation in Neural Networks for faster models
arXiv.org · 2015
353
cited
Metrics for Finite Markov Decision Processes
AAAI Conference on Artificial Intelligence · 2004
344
cited
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