Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Shpilman, Aleksei"'
Autor:
Grigoryev, Timofey, Verezemskaya, Polina, Krinitskiy, Mikhail, Anikin, Nikita, Gavrikov, Alexander, Trofimov, Ilya, Balabin, Nikita, Shpilman, Aleksei, Eremchenko, Andrei, Gulev, Sergey, Burnaev, Evgeny, Vanovskiy, Vladimir
Global warming made the Arctic available for marine operations and created demand for reliable operational sea ice forecasts to make them safe. While ocean-ice numerical models are highly computationally intensive, relatively lightweight ML-based met
Externí odkaz:
http://arxiv.org/abs/2210.08877
Autor:
Egorov, Vladimir, Shpilman, Aleksei
Recent Multi-Agent Reinforcement Learning (MARL) literature has been largely focused on Centralized Training with Decentralized Execution (CTDE) paradigm. CTDE has been a dominant approach for both cooperative and mixed environments due to its capabi
Externí odkaz:
http://arxiv.org/abs/2205.15023
Autor:
Eichenberger, Christian, Neun, Moritz, Martin, Henry, Herruzo, Pedro, Spanring, Markus, Lu, Yichao, Choi, Sungbin, Konyakhin, Vsevolod, Lukashina, Nina, Shpilman, Aleksei, Wiedemann, Nina, Raubal, Martin, Wang, Bo, Vu, Hai L., Mohajerpoor, Reza, Cai, Chen, Kim, Inhi, Hermes, Luca, Melnik, Andrew, Velioglu, Riza, Vieth, Markus, Schilling, Malte, Bojesomo, Alabi, Marzouqi, Hasan Al, Liatsis, Panos, Santokhi, Jay, Hillier, Dylan, Yang, Yiming, Sarwar, Joned, Jordan, Anna, Hewage, Emil, Jonietz, David, Tang, Fei, Gruca, Aleksandra, Kopp, Michael, Kreil, David, Hochreiter, Sepp
The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that neural networks can successfully predict future traffic conditions 1 hour into the future on simply aggregated GPS probe data in time and space bins. We thus reinterpreted the c
Externí odkaz:
http://arxiv.org/abs/2203.17070
Despite the numerous breakthroughs achieved with Reinforcement Learning (RL), solving environments with sparse rewards remains a challenging task that requires sophisticated exploration. Learning from Demonstrations (LfD) remedies this issue by guidi
Externí odkaz:
http://arxiv.org/abs/2203.10905
When dealing with binary classification of data with only one labeled class data scientists employ two main approaches, namely One-Class (OC) classification and Positive Unlabeled (PU) learning. The former only learns from labeled positive data, wher
Externí odkaz:
http://arxiv.org/abs/2203.07206
Autor:
Kanervisto, Anssi, Milani, Stephanie, Ramanauskas, Karolis, Topin, Nicholay, Lin, Zichuan, Li, Junyou, Shi, Jianing, Ye, Deheng, Fu, Qiang, Yang, Wei, Hong, Weijun, Huang, Zhongyue, Chen, Haicheng, Zeng, Guangjun, Lin, Yue, Micheli, Vincent, Alonso, Eloi, Fleuret, François, Nikulin, Alexander, Belousov, Yury, Svidchenko, Oleg, Shpilman, Aleksei
Reinforcement learning competitions advance the field by providing appropriate scope and support to develop solutions toward a specific problem. To promote the development of more broadly applicable methods, organizers need to enforce the use of gene
Externí odkaz:
http://arxiv.org/abs/2202.10583
Autor:
Svidchenko, Oleg, Shpilman, Aleksei
Recent advances in reinforcement learning have demonstrated its ability to solve hard agent-environment interaction tasks on a super-human level. However, the application of reinforcement learning methods to practical and real-world tasks is currentl
Externí odkaz:
http://arxiv.org/abs/2112.01195
Autor:
Zenkova, Natalia, Sedykh, Ekaterina, Shugaeva, Tatiana, Strashko, Vladislav, Ermak, Timofei, Shpilman, Aleksei
Predicting a structure of an antibody from its sequence is important since it allows for a better design process of synthetic antibodies that play a vital role in the health industry. Most of the structure of an antibody is conservative. The most var
Externí odkaz:
http://arxiv.org/abs/2111.10656
In this technical report, we present our solution to the Traffic4Cast 2021 Core Challenge, in which participants were asked to develop algorithms for predicting a traffic state 60 minutes ahead, based on the information from the previous hour, in 4 d
Externí odkaz:
http://arxiv.org/abs/2111.03421
Autor:
Laurent, Florian, Schneider, Manuel, Scheller, Christian, Watson, Jeremy, Li, Jiaoyang, Chen, Zhe, Zheng, Yi, Chan, Shao-Hung, Makhnev, Konstantin, Svidchenko, Oleg, Egorov, Vladimir, Ivanov, Dmitry, Shpilman, Aleksei, Spirovska, Evgenija, Tanevski, Oliver, Nikov, Aleksandar, Grunder, Ramon, Galevski, David, Mitrovski, Jakov, Sartoretti, Guillaume, Luo, Zhiyao, Damani, Mehul, Bhattacharya, Nilabha, Agarwal, Shivam, Egli, Adrian, Nygren, Erik, Mohanty, Sharada
The Flatland competition aimed at finding novel approaches to solve the vehicle re-scheduling problem (VRSP). The VRSP is concerned with scheduling trips in traffic networks and the re-scheduling of vehicles when disruptions occur, for example the br
Externí odkaz:
http://arxiv.org/abs/2103.16511