4 code implementations • 14 Jun 2024 • Yuri Kuratov, Aydar Bulatov, Petr Anokhin, Ivan Rodkin, Dmitry Sorokin, Artyom Sorokin, Mikhail Burtsev
The BABILong benchmark is extendable to any length to support the evaluation of new upcoming models with increased capabilities, and we provide splits up to 10 million token lengths.
2 code implementations • 16 Feb 2024 • Yuri Kuratov, Aydar Bulatov, Petr Anokhin, Dmitry Sorokin, Artyom Sorokin, Mikhail Burtsev
This paper addresses the challenge of processing long documents using generative transformer models.
1 code implementation • 9 Jun 2023 • Dmitry Sorokin, Alexander Kostin
Combinatorial optimization problems require an exhaustive search to find the optimal solution.
1 code implementation • 22 Mar 2022 • Eric Hambro, Sharada Mohanty, Dmitrii Babaev, Minwoo Byeon, Dipam Chakraborty, Edward Grefenstette, Minqi Jiang, DaeJin Jo, Anssi Kanervisto, Jongmin Kim, Sungwoong Kim, Robert Kirk, Vitaly Kurin, Heinrich Küttler, Taehwon Kwon, Donghoon Lee, Vegard Mella, Nantas Nardelli, Ivan Nazarov, Nikita Ovsov, Jack Parker-Holder, Roberta Raileanu, Karolis Ramanauskas, Tim Rocktäschel, Danielle Rothermel, Mikayel Samvelyan, Dmitry Sorokin, Maciej Sypetkowski, Michał Sypetkowski
In this report, we summarize the takeaways from the first NeurIPS 2021 NetHack Challenge.
1 code implementation • 9 Jul 2021 • Stepan Makarenko, Dmitry Sorokin, Alexander Ulanov, A. I. Lvovsky
Reinforcement learning is finding its way to real-world problem application, transferring from simulated environments to physical setups.
no code implementations • 8 Jul 2021 • Arsen Kuzhamuratov, Dmitry Sorokin, Alexander Ulanov, A. I. Lvovsky
Animals have remarkable abilities to adapt locomotion to different terrains and tasks.
1 code implementation • NeurIPS 2020 • Dmitry Sorokin, Alexander Ulanov, Ekaterina Sazhina, Alexander Lvovsky
Limitations in acquiring training data restrict potential applications of deep reinforcement learning (RL) methods to the training of real-world robots.
no code implementations • ICML 2017 • Riad Akrour, Dmitry Sorokin, Jan Peters, Gerhard Neumann
Bayesian optimization is renowned for its sample efficiency but its application to higher dimensional tasks is impeded by its focus on global optimization.