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 | 1996 | 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 | 177 | 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 | 111* | 2019 |
On characterizing the capacity of neural networks using algebraic topology WH Guss, R Salakhutdinov arXiv preprint arXiv:1802.04443, 2018 | 97 | 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 | 55 | 2020 |
Evaluating large language models trained on code. arXiv 2021 M Chen, J Tworek, H Jun, Q Yuan, HPO Pinto, J Kaplan, H Edwards, ... arXiv preprint arXiv:2107.03374 10, 2021 | 42 | 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 | 32* | 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 | 26 | 2021 |
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 | 25 | 2021 |
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 | 20 | 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 | 15 | 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 | 15 | 2019 |
Deep function machines: Generalized neural networks for topological layer expression WH Guss arXiv preprint arXiv:1612.04799, 2016 | 14 | 2016 |
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 | 10 | 2021 |
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 |
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 |