| Learning to run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments Ł Kidziński, SP Mohanty, CF Ong, Z Huang, S Zhou, A Pechenko, ... The NIPS'17 Competition: Building Intelligent Systems, 121-153, 2018 | 56 | 2018 |
| Artificial intelligence for prosthetics: Challenge solutions Ł Kidziński, C Ong, SP Mohanty, J Hicks, S Carroll, B Zhou, H Zeng, ... The NeurIPS'18 Competition, 69-128, 2020 | 15 | 2020 |
| Run, skeleton, run: skeletal model in a physics-based simulation M Pavlov, S Kolesnikov, S Plis 2018 AAAI Spring Symposium Series, 2018 | 10 | 2018 |
| Catalyst. RL: a distributed framework for reproducible RL research S Kolesnikov, O Hrinchuk arXiv preprint arXiv:1903.00027, 2019 | 5 | 2019 |
| Sample Efficient Ensemble Learning with Catalyst. RL S Kolesnikov, V Khrulkov arXiv preprint arXiv:2003.14210, 2020 | 3 | 2020 |
| Showing your offline reinforcement learning work: Online evaluation budget matters V Kurenkov, S Kolesnikov arXiv preprint arXiv:2110.04156, 2021 | 1 | 2021 |
| TTRS: Tinkoff Transactions Recommender System benchmark S Kolesnikov, O Lashinin, M Pechatov, A Kosov arXiv preprint arXiv:2110.05589, 2021 | | 2021 |
| LRWR: Large-Scale Benchmark for Lip Reading in Russian language E Egorov, V Kostyumov, M Konyk, S Kolesnikov arXiv preprint arXiv:2109.06692, 2021 | | 2021 |
Sergey PlisTReNDS center: GSU, Emory, and GATechVerified email at gsu.edu
