| 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 | 642 | 2021 |
| MineRL: A Large-Scale Dataset of Minecraft Demonstrations WH Guss*, B Houghton*, N Topin, P Wang, C Codel, M Veloso, ... Conference: Twenty-Eighth International Joint Conference on Artificial …, 2019 | 109 | 2019 |
| The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors WH Guss, C Codel, K Hofmann, B Houghton, N Kuno, S Milani, ... NeurIPS 2019, 2019 | 90* | 2019 |
| On characterizing the capacity of neural networks using algebraic topology WH Guss, R Salakhutdinov arXiv preprint arXiv:1802.04443, 2018 | 88 | 2018 |
| Searchable database of trained artificial intelligence objects that can be reused, reconfigured, and recomposed, into one or more subsequent artificial intelligence models MI Hammond, KME Browne, M Campos, MJ Brown, R Kong, W Guss, ... US Patent 10,586,173, 2020 | 39 | 2020 |
| The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors WH Guss, MY Castro, S Devlin, B Houghton, NS Kuno, C Loomis, S Milani, ... arXiv preprint arXiv:2101.11071, 2021 | 23 | 2021 |
| Retrospective analysis of the 2019 MineRL competition on sample efficient reinforcement learning S Milani, N Topin, B Houghton, WH Guss, SP Mohanty, K Nakata, ... NeurIPS 2019 Competition and Demonstration Track, 203-214, 2020 | 21* | 2020 |
| The MineRL BASALT competition on learning from human feedback R Shah, C Wild, SH Wang, N Alex, B Houghton, W Guss, S Mohanty, ... arXiv preprint arXiv:2107.01969, 2021 | 18 | 2021 |
| Evaluating large language models trained on code.(2021) M Chen, J Tworek, H Jun, Q Yuan, HP de Oliveira Pinto, J Kaplan, ... arXiv preprint arXiv:2107.03374, 2021 | 17 | 2021 |
| On universal approximation by neural networks with uniform guarantees on approximation of infinite dimensional maps WH Guss, R Salakhutdinov arXiv preprint arXiv:1910.01545, 2019 | 14 | 2019 |
| Measuring sample efficiency and generalization in reinforcement learning benchmarks: Neurips 2020 procgen benchmark S Mohanty, J Poonganam, A Gaidon, A Kolobov, B Wulfe, D Chakraborty, ... arXiv preprint arXiv:2103.15332, 2021 | 12 | 2021 |
| Deep function machines: Generalized neural networks for topological layer expression WH Guss arXiv preprint arXiv:1612.04799, 2016 | 12 | 2016 |
| Guaranteeing reproducibility in deep learning competitions B Houghton, S Milani, N Topin, W Guss, K Hofmann, D Perez-Liebana, ... arXiv preprint arXiv:2005.06041, 2020 | 10 | 2020 |
| Towards robust and domain agnostic reinforcement learning competitions: MineRL 2020 WH Guss, S Milani, N Topin, B Houghton, S Mohanty, A Melnik, A Harter, ... NeurIPS 2020 Competition and Demonstration Track, 233-252, 2021 | 8* | 2021 |
| Multi-task curriculum learning in a complex, visual, hard-exploration domain: Minecraft I Kanitscheider, J Huizinga, D Farhi, WH Guss, B Houghton, R Sampedro, ... arXiv preprint arXiv:2106.14876, 2021 | 7 | 2021 |
| Eigen: A Step Towards Conversational AI WH Guss, J Bartlett, P Kuznetsov, P Patil Alexa Prize Proceedings 2017 1 (1), 1-8, 2017 | 6 | 2017 |
Brandon HoughtonCarnegie MellonVerified email at cmu.edu
