| Abandoning objectives: Evolution through the search for novelty alone J Lehman, KO Stanley Evolutionary computation 19 (2), 189-223, 2011 | 1221 | 2011 |
| An intriguing failing of convolutional neural networks and the coordconv solution R Liu, J Lehman, P Molino, F Petroski Such, E Frank, A Sergeev, ... Advances in neural information processing systems 31, 2018 | 1023 | 2018 |
| Deep neuroevolution: Genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning FP Such, V Madhavan, E Conti, J Lehman, KO Stanley, J Clune arXiv preprint arXiv:1712.06567, 2017 | 942 | 2017 |
| Designing neural networks through neuroevolution KO Stanley, J Clune, J Lehman, R Miikkulainen Nature Machine Intelligence 1 (1), 24-35, 2019 | 789 | 2019 |
| Exploiting open-endedness to solve problems through the search for novelty. J Lehman, KO Stanley ALIFE, 329-336, 2008 | 719 | 2008 |
| Evolving a diversity of virtual creatures through novelty search and local competition J Lehman, KO Stanley Proceedings of the 13th annual conference on Genetic and evolutionary …, 2011 | 565 | 2011 |
| Go-explore: a new approach for hard-exploration problems A Ecoffet, J Huizinga, J Lehman, KO Stanley, J Clune arXiv preprint arXiv:1901.10995, 2019 | 451 | 2019 |
| Improving exploration in evolution strategies for deep reinforcement learning via a population of novelty-seeking agents E Conti, V Madhavan, F Petroski Such, J Lehman, K Stanley, J Clune Advances in neural information processing systems 31, 2018 | 426 | 2018 |
| First return, then explore A Ecoffet, J Huizinga, J Lehman, KO Stanley, J Clune Nature 590 (7847), 580-586, 2021 | 406 | 2021 |
| The surprising creativity of digital evolution: A collection of anecdotes from the evolutionary computation and artificial life research communities J Lehman, J Clune, D Misevic, C Adami, L Altenberg, J Beaulieu, ... Artificial life 26 (2), 274-306, 2020 | 361 | 2020 |
| Paired open-ended trailblazer (poet): Endlessly generating increasingly complex and diverse learning environments and their solutions R Wang, J Lehman, J Clune, KO Stanley arXiv preprint arXiv:1901.01753, 2019 | 271 | 2019 |
| A neuroevolution approach to general atari game playing M Hausknecht, J Lehman, R Miikkulainen, P Stone IEEE Transactions on Computational Intelligence and AI in Games 6 (4), 355-366, 2014 | 259 | 2014 |
| Novelty search and the problem with objectives J Lehman, KO Stanley Genetic programming theory and practice IX, 37-56, 2011 | 220 | 2011 |
| Why greatness cannot be planned: The myth of the objective KO Stanley, J Lehman Springer, 2015 | 199 | 2015 |
| Learning to continually learn S Beaulieu, L Frati, T Miconi, J Lehman, KO Stanley, J Clune, N Cheney ECAI 2020, 992-1001, 2020 | 191 | 2020 |
| Generative teaching networks: Accelerating neural architecture search by learning to generate synthetic training data FP Such, A Rawal, J Lehman, K Stanley, J Clune International Conference on Machine Learning, 9206-9216, 2020 | 186 | 2020 |
| Revising the evolutionary computation abstraction: minimal criteria novelty search J Lehman, KO Stanley Proceedings of the 12th annual conference on Genetic and evolutionary …, 2010 | 158 | 2010 |
| Efficiently evolving programs through the search for novelty J Lehman, KO Stanley Proceedings of the 12th annual conference on Genetic and evolutionary …, 2010 | 154 | 2010 |
| Enhanced poet: Open-ended reinforcement learning through unbounded invention of learning challenges and their solutions R Wang, J Lehman, A Rawal, J Zhi, Y Li, J Clune, K Stanley International conference on machine learning, 9940-9951, 2020 | 127 | 2020 |
| Safe mutations for deep and recurrent neural networks through output gradients J Lehman, J Chen, J Clune, KO Stanley Proceedings of the Genetic and Evolutionary Computation Conference, 117-124, 2018 | 113 | 2018 |
Kenneth O. StanleyResearch Manager, OpenAIopenai.com의 이메일 확인됨