| Addressing Function Approximation Error in Actor-Critic Methods S Fujimoto, H van Hoof, D Meger Proceedings of the 35th International Conference on Machine Learning 80 …, 2018 | 431 | 2018 |
| Off-Policy Deep Reinforcement Learning without Exploration S Fujimoto, D Meger, D Precup Proceedings of the 36th International Conference on Machine Learning 97 …, 2019 | 92 | 2019 |
| Horizon: Facebook's Open Source Applied Reinforcement Learning Platform J Gauci, E Conti, Y Liang, K Virochsiri, Y He, Z Kaden, V Narayanan, X Ye, ... Reinforcement Learning for Real Life Workshop in the 36th International …, 2019 | 44 | 2019 |
| GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects EJ Smith, S Fujimoto, A Romero, D Meger Proceedings of the 36th International Conference on Machine Learning 97 …, 2019 | 20* | 2019 |
| Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation E Smith, S Fujimoto, D Meger Advances in Neural Information Processing Systems 31, 6477-6487, 2018 | 18 | 2018 |
| Sentiment analysis: It’s complicated! K Kenyon-Dean, E Ahmed, S Fujimoto, J Georges-Filteau, C Glasz, ... Proceedings of the 2018 Conference of the North American Chapter of the …, 2018 | 14 | 2018 |
| Benchmarking Batch Deep Reinforcement Learning Algorithms S Fujimoto, E Conti, M Ghavamzadeh, J Pineau Deep Reinforcement Learning Workshop NeurIPS 2019, 2019 | 11 | 2019 |
| An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay S Fujimoto, D Meger, D Precup arXiv preprint arXiv:2007.06049, 2020 | | 2020 |
David MegerAssistant Professor at McGill UniversityVerified email at cim.mcgill.ca
