| Kernel-based fuzzy clustering and fuzzy clustering: A comparative experimental study D Graves, W Pedrycz Fuzzy sets and systems 161 (4), 522-543, 2010 | 402 | 2010 |
| Smarts: Scalable multi-agent reinforcement learning training school for autonomous driving M Zhou, J Luo, J Villella, Y Yang, D Rusu, J Miao, W Zhang, M Alban, ... arXiv preprint arXiv:2010.09776, 2020 | 114 | 2020 |
| Fuzzy prediction architecture using recurrent neural networks D Graves, W Pedrycz Neurocomputing 72 (7-9), 1668-1678, 2009 | 67 | 2009 |
| Fuzzy c-means, gustafson-kessel fcm, and kernel-based fcm: A comparative study D Graves, W Pedrycz Analysis and design of intelligent systems using soft computing techniques …, 2007 | 54 | 2007 |
| A clustering-based graph Laplacian framework for value function approximation in reinforcement learning X Xu, Z Huang, D Graves, W Pedrycz IEEE Transactions on Cybernetics 44 (12), 2613-2625, 2014 | 39 | 2014 |
| Mapless navigation among dynamics with social-safety-awareness: a reinforcement learning approach from 2d laser scans J Jin, NM Nguyen, N Sakib, D Graves, H Yao, M Jagersand 2020 IEEE international conference on robotics and automation (ICRA), 6979-6985, 2020 | 37 | 2020 |
| Importance resampling for off-policy prediction M Schlegel, W Chung, D Graves, J Qian, M White Advances in Neural Information Processing Systems 32, 2019 | 34 | 2019 |
| Diverse auto-curriculum is critical for successful real-world multiagent learning systems Y Yang, J Luo, Y Wen, O Slumbers, D Graves, HB Ammar, J Wang, ... arXiv preprint arXiv:2102.07659, 2021 | 31 | 2021 |
| Diverse auto-curriculum is critical for successful real-world multiagent learning systems Y Yang, J Luo, Y Wen, O Slumbers, D Graves, HB Ammar, J Wang, ... arXiv preprint arXiv:2102.07659, 2021 | 31 | 2021 |
| Performance of kernel-based fuzzy clustering D Graves, W Pedrycz Electronics Letters 43 (25), 1, 2007 | 30 | 2007 |
| Fixed-horizon temporal difference methods for stable reinforcement learning K De Asis, A Chan, S Pitis, R Sutton, D Graves Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 3741-3748, 2020 | 25 | 2020 |
| A survey and formal analyses on sequence learning methodologies and deep neural networks Y Wang, H Leung, M Gavrilova, O Zatarain, D Graves, J Lu, N Howard, ... 2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive …, 2018 | 25 | 2018 |
| Multivariate Segmentation of Time Series with Differential Evolution. D Graves, W Pedrycz IFSA/EUSFLAT Conf., 1108-1113, 2009 | 21 | 2009 |
| Clustering with proximity knowledge and relational knowledge D Graves, J Noppen, W Pedrycz Pattern recognition 45 (7), 2633-2644, 2012 | 14 | 2012 |
| Sequence Learning for Images Recognition in Videos with Differential Neural Networks Y Wang, O Zatarain, T Tsai, D Graves 2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive …, 2019 | 13 | 2019 |
| What about inputting policy in value function: Policy representation and policy-extended value function approximator H Tang, Z Meng, J Hao, C Chen, D Graves, D Li, C Yu, H Mao, W Liu, ... Proceedings of the AAAI Conference on Artificial Intelligence 36 (8), 8441-8449, 2022 | 12 | 2022 |
| Perception as prediction using general value functions in autonomous driving applications D Graves, K Rezaee, S Scheideman 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2019 | 12 | 2019 |
| Proximity fuzzy clustering and its application to time series clustering and prediction D Graves, W Pedrycz 2010 10th International Conference on Intelligent Systems Design and …, 2010 | 12 | 2010 |
| Smarts: An open-source scalable multi-agent rl training school for autonomous driving M Zhou, J Luo, J Villella, Y Yang, D Rusu, J Miao, W Zhang, M Alban, ... Conference on Robot Learning, 264-285, 2021 | 11 | 2021 |
| Offline learning of counterfactual perception as prediction for real-world robotic reinforcement learning J Jin, D Graves, C Haigh, J Luo, M Jagersand arXiv preprint arXiv:2011.05857, 2020 | 11 | 2020 |
