| Neural message passing for quantum chemistry J Gilmer, SS Schoenholz, PF Riley, O Vinyals, GE Dahl International Conference on Machine Learning, 1263-1272, 2017 | 1805 | 2017 |
| Molecular graph convolutions: moving beyond fingerprints S Kearnes, K McCloskey, M Berndl, V Pande, P Riley Journal of computer-aided molecular design 30 (8), 595-608, 2016 | 706 | 2016 |
| Massively multitask networks for drug discovery B Ramsundar, S Kearnes, P Riley, D Webster, D Konerding, V Pande arXiv preprint arXiv:1502.02072, 2015 | 360 | 2015 |
| Prediction errors of molecular machine learning models lower than hybrid DFT error FA Faber, L Hutchison, B Huang, J Gilmer, SS Schoenholz, GE Dahl, ... Journal of chemical theory and computation 13 (11), 5255-5264, 2017 | 358* | 2017 |
| Design and philosophy of the bimasc, a highly dynamic biped JW Hurst, JE Chestnutt, AA Rizzi Proceedings 2007 IEEE International Conference on Robotics and Automation …, 2007 | 172* | 2007 |
| AAM derived face representations for robust facial action recognition S Lucey, I Matthews, C Hu, Z Ambadar, F De la Torre, J Cohn 7th International Conference on Automatic Face and Gesture Recognition …, 2006 | 140 | 2006 |
| Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds N Thomas, T Smidt, S Kearnes, L Yang, L Li, K Kohlhoff, P Riley arXiv preprint arXiv:1802.08219, 2018 | 135 | 2018 |
| Optimization of molecules via deep reinforcement learning Z Zhou, S Kearnes, L Li, RN Zare, P Riley Scientific reports 9 (1), 1-10, 2019 | 127 | 2019 |
| Spades-a distributed agent simulation environment with software-in-the-loop execution PF Riley, GF Riley Georgia Institute of Technology, 2003 | 124 | 2003 |
| The CMUnited-99 champion simulator team P Stone, P Riley, M Veloso Robot Soccer World Cup, 35-48, 1999 | 122 | 1999 |
| The CMUnited-98 champion simulator team P Stone, M Veloso, P Riley Robot Soccer World Cup, 61-76, 1998 | 107 | 1998 |
| On behavior classification in adversarial environments P Riley, M Veloso Distributed autonomous robotic systems 4, 371-380, 2000 | 105 | 2000 |
| Defining and using ideal teammate and opponent agent models: A case study in robotic soccer P Stone, P Riley, M Veloso Proceedings Fourth International Conference on MultiAgent Systems, 441-442, 2000 | 94 | 2000 |
| Planning for Distributed Execution through Use of Probabilistic Opponent Models. P Riley, MM Veloso AIPS, 72-82, 2002 | 79 | 2002 |
| Recognizing probabilistic opponent movement models P Riley, M Veloso Robot Soccer World Cup, 453-458, 2001 | 77 | 2001 |
| An empirical study of coaching P Riley, M Veloso, G Kaminka Distributed Autonomous Robotic Systems 5, 215-224, 2002 | 65 | 2002 |
| Three pitfalls to avoid in machine learning P Riley Nature 572 (7767), 27-29, 2019 | 63 | 2019 |
| Proceedings of the 34th International Conference on Machine Learning J Gilmer, SS Schoenholz, PF Riley, O Vinyals, GE Dahl PMLR 70, 1263-1272, 2017 | 58 | 2017 |
| Layered disclosure: Revealing agents’ internals P Riley, P Stone, M Veloso International Workshop on Agent Theories, Architectures, and Languages, 61-72, 2000 | 52 | 2000 |
| Users manual: RoboCup soccer server manual for soccer server version 7.07 and later M Chen, E Foroughi, F Heintz, S Kapetanakis, K Kostiadis, J Kummeneje, ... | 36 | 2003 |
