| 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 | 756 | 2017 |
| 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 | 717 | 2018 |
| Evaluating large language models trained on code M Chen, J Tworek, H Jun, Q Yuan, HPO Pinto, J Kaplan, H Edwards, ... arXiv preprint arXiv:2107.03374, 2021 | 629 | 2021 |
| 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 | 349 | 2018 |
| Robust spatial filtering with graph convolutional neural networks FP Such, S Sah, MA Dominguez, S Pillai, C Zhang, A Michael, ND Cahill, ... IEEE Journal of Selected Topics in Signal Processing 11 (6), 884-896, 2017 | 160 | 2017 |
| Intelligent character recognition using fully convolutional neural networks R Ptucha, FP Such, S Pillai, F Brockler, V Singh, P Hutkowski Pattern recognition 88, 604-613, 2019 | 129 | 2019 |
| 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 | 105 | 2020 |
| Text and code embeddings by contrastive pre-training A Neelakantan, T Xu, R Puri, A Radford, JM Han, J Tworek, Q Yuan, ... arXiv preprint arXiv:2201.10005, 2022 | 65 | 2022 |
| An atari model zoo for analyzing, visualizing, and comparing deep reinforcement learning agents FP Such, V Madhavan, R Liu, R Wang, PS Castro, Y Li, J Zhi, L Schubert, ... arXiv preprint arXiv:1812.07069, 2018 | 53 | 2018 |
| Generalized hidden parameter mdps: Transferable model-based rl in a handful of trials C Perez, FP Such, T Karaletsos Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5403-5411, 2020 | 29 | 2020 |
| Fully convolutional networks for handwriting recognition FP Such, D Peri, F Brockler, H Paul, R Ptucha 2018 16th International Conference on Frontiers in Handwriting Recognition …, 2018 | 26 | 2018 |
| System and method of character recognition using fully convolutional neural networks with attention FP Such, R Ptucha, F Brockler, P Hutkowski US Patent 10,846,523, 2020 | 22 | 2020 |
| Towards 3d convolutional neural networks with meshes M Dominguez, FP Such, S Sah, R Ptucha 2017 IEEE International Conference on Image Processing (ICIP), 3929-3933, 2017 | 16 | 2017 |
| Efficient transfer learning and online adaptation with latent variable models for continuous control CF Perez, FP Such, T Karaletsos arXiv preprint arXiv:1812.03399, 2018 | 15 | 2018 |
| Synthetic petri dish: a novel surrogate model for rapid architecture search A Rawal, J Lehman, FP Such, J Clune, KO Stanley arXiv preprint arXiv:2005.13092, 2020 | 5 | 2020 |
| Temporally steered gaussian attention for video understanding S Sah, T Nguyen, M Dominguez, F Petroski Such, R Ptucha Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 3 | 2017 |
| Generating training datasets for training neural networks FP Such, A Rawal, JA Lehman, KO Stanley, JM Clune US Patent App. 16/746,674, 2020 | 2 | 2020 |
| System and method of character recognition using fully convolutional neural networks FP Such, R Ptucha, F Brockler, P Hutkowski, V Singh US Patent 10,936,862, 2021 | 1 | 2021 |
| Model based reinforcement learning based on generalized hidden parameter markov decision processes T Karaletsos, FP Such, CF Perez US Patent App. 16/881,557, 2020 | 1 | 2020 |
| Scalable parameter encoding of artificial neural networks obtained via an evolutionary process FP Such, JM Clune, KO Stanley, E Conti, V Madhavan, JA Lehman US Patent 10,599,975, 2020 | 1 | 2020 |
