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Aleksei Shpilman
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2020 – today
- 2023
- [c18]Anastasiia Filatova, Mikhail V. Kovalchuk, Stanislav Batalenkov, Aleksander Voskresenskiy, Irina Deeva, Anna V. Kaluzhnaya, Aleksei Shpilman, Natalia Kondrashova, Maxim Dudnichenko, Denis A. Nasonov:
A Multi-Contractor Approach for MLRCPSP with the Graph Structure Optimization. CEC 2023: 1-8 - 2022
- [j2]Nina Lukashina
, Elena Kartysheva
SimVec: predicting polypharmacy side effects for new drugs. J. Cheminformatics 14(1): 49 (2022) - [j1]Timofey Grigoryev
Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting. Remote. Sens. 14(22): 5837 (2022) - [c17]Vladimir Egorov, Alexey Shpilman:
Scalable Multi-Agent Model-Based Reinforcement Learning. AAMAS 2022: 381-390 - [i25]Anssi Kanervisto, Stephanie Milani, Karolis Ramanauskas, Nicholay Topin, Zichuan Lin, Junyou Li, Jianing Shi, Deheng Ye, Qiang Fu, Wei Yang, Weijun Hong, Zhongyue Huang, Haicheng Chen, Guangjun Zeng, Yue Lin, Vincent Micheli, Eloi Alonso, François Fleuret, Alexander Nikulin, Yury Belousov, Oleg Svidchenko, Aleksei Shpilman:
MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned. CoRR abs/2202.10583 (2022) - [i24]Farid Bagirov, Dmitry Ivanov, Aleksei Shpilman:
Improving State-of-the-Art in One-Class Classification by Leveraging Unlabeled Data. CoRR abs/2203.07206 (2022) - [i23]Georgiy Pshikhachev, Dmitry Ivanov, Vladimir Egorov, Aleksei Shpilman:
Self-Imitation Learning from Demonstrations. CoRR abs/2203.10905 (2022) - [i22]Christian Eichenberger
Traffic4cast at NeurIPS 2021 - Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes. CoRR abs/2203.17070 (2022) - [i21]Vladimir Egorov, Aleksei Shpilman:
Scalable Multi-Agent Model-Based Reinforcement Learning. CoRR abs/2205.15023 (2022) - [i20]Timofey Grigoryev, Polina Verezemskaya
Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting. CoRR abs/2210.08877 (2022) - 2021
- [c16]Dmitry Ivanov, Vladimir Egorov, Aleksei Shpilman:
Balancing Rational and Other-Regarding Preferences in Cooperative-Competitive Environments. AAMAS 2021: 1536-1538 - [c15]Anssi Kanervisto, Stephanie Milani, Karolis Ramanauskas, Nicholay Topin, Zichuan Lin, Junyou Li, Jianing Shi, Deheng Ye, Qiang Fu, Wei Yang, Weijun Hong, Zhongyue Huang, Haicheng Chen, Guangjun Zeng, Yue Lin, Vincent Micheli, Eloi Alonso, François Fleuret, Alexander Nikulin, Yury Belousov, Oleg Svidchenko, Aleksei Shpilman:
MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned. NeurIPS (Competition and Demos) 2021: 13-28 - [c14]Christian Eichenberger, Moritz Neun, Henry Martin, Pedro Herruzo, Markus Spanring, Yichao Lu, Sungbin Choi, Vsevolod Konyakhin, Nina Lukashina, Aleksei Shpilman, Nina Wiedemann, Martin Raubal, Bo Wang
Traffic4cast at NeurIPS 2021 - Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes. NeurIPS (Competition and Demos) 2021: 97-112 - [i19]Dmitry Ivanov, Vladimir Egorov, Aleksei Shpilman:
Balancing Rational and Other-Regarding Preferences in Cooperative-Competitive Environments. CoRR abs/2102.12307 (2021) - [i18]Florian Laurent, Manuel Schneider, Christian Scheller, Jeremy D. Watson, Jiaoyang Li, Zhe Chen, Yi Zheng, Shao-Hung Chan, Konstantin Makhnev, Oleg Svidchenko, Vladimir Egorov, Dmitry Ivanov, Aleksei Shpilman, Evgenija Spirovska, Oliver Tanevski, Aleksandar Nikov, Ramon Grunder, David Galevski, Jakov Mitrovski, Guillaume Sartoretti, Zhiyao Luo, Mehul Damani, Nilabha Bhattacharya, Shivam Agarwal, Adrian Egli, Erik Nygren, Sharada P. Mohanty:
Flatland Competition 2020: MAPF and MARL for Efficient Train Coordination on a Grid World. CoRR abs/2103.16511 (2021) - [i17]Artyom Lobanov, Timofey Bryksin, Alexey Shpilman:
Automatic Classification of Error Types in Solutions to Programming Assignments at Online Learning Platform. CoRR abs/2107.06009 (2021) - [i16]Vsevolod Konyakhin, Nina Lukashina, Aleksei Shpilman:
Solving Traffic4Cast Competition with U-Net and Temporal Domain Adaptation. CoRR abs/2111.03421 (2021) - [i15]Natalia Zenkova, Ekaterina Sedykh, Tatiana Shugaeva, Vladislav Strashko, Timofei Ermak, Aleksei Shpilman:
Simple End-to-end Deep Learning Model for CDR-H3 Loop Structure Prediction. CoRR abs/2111.10656 (2021) - [i14]Oleg Svidchenko, Aleksei Shpilman:
Maximum Entropy Model-based Reinforcement Learning. CoRR abs/2112.01195 (2021) - 2020
- [c13]Anna Nikiforovskaya, Nikolai Kapralov
Automatic generation of reviews of scientific papers. ICMLA 2020: 314-319 - [c12]Timofey Bryksin, Victor Petukhov, Ilya Alexin, Stanislav Prikhodko, Alexey Shpilman, Vladimir Kovalenko, Nikita Povarov:
Using Large-Scale Anomaly Detection on Code to Improve Kotlin Compiler. MSR 2020: 455-465 - [c11]Mikita Sazanovich, Anastasiya Nikolskaya, Yury Belousov, Aleksei Shpilman:
Solving Black-Box Optimization Challenge via Learning Search Space Partition for Local Bayesian Optimization. NeurIPS (Competition and Demos) 2020: 77-85 - [c10]Florian Laurent, Manuel Schneider, Christian Scheller, Jeremy D. Watson, Jiaoyang Li, Zhe Chen, Yi Zheng, Shao-Hung Chan, Konstantin Makhnev, Oleg Svidchenko, Vladimir Egorov, Dmitry Ivanov, Aleksei Shpilman, Evgenija Spirovska, Oliver Tanevski, Aleksandar Nikov, Ramon Grunder, David Galevski, Jakov Mitrovski, Guillaume Sartoretti, Zhiyao Luo, Mehul Damani, Nilabha Bhattacharya, Shivam Agarwal, Adrian Egli, Erik Nygren, Sharada P. Mohanty:
Flatland Competition 2020: MAPF and MARL for Efficient Train Coordination on a Grid World. NeurIPS (Competition and Demos) 2020: 275-301 - [i13]Timofey Bryksin, Victor Petukhov, Ilya Alexin, Stanislav Prikhodko, Alexey Shpilman, Vladimir Kovalenko, Nikita Povarov:
Using Large-Scale Anomaly Detection on Code to Improve Kotlin Compiler. CoRR abs/2004.01618 (2020) - [i12]Mikita Sazanovich, Konstantin Chaika, Kirill Krinkin, Aleksei Shpilman:
Imitation Learning Approach for AI Driving Olympics Trained on Real-world and Simulation Data Simultaneously. CoRR abs/2007.03514 (2020) - [i11]Anna Nikiforovskaya, Nikolai Kapralov, Anna Vlasova, Oleg Shpynov, Aleksei Shpilman:
Automatic generation of reviews of scientific papers. CoRR abs/2010.04147 (2020) - [i10]Nina Lukashina, Alisa Alenicheva, Elizaveta Vlasova, Artem Kondiukov, Aigul Khakimova, Emil Magerramov, Nikita Churikov, Aleksei Shpilman:
Lipophilicity Prediction with Multitask Learning and Molecular Substructures Representation. CoRR abs/2011.12117 (2020) - [i9]Ivan Sosin, Daniel Kudenko, Aleksei Shpilman:
Continuous Gesture Recognition from sEMG Sensor Data with Recurrent Neural Networks and Adversarial Domain Adaptation. CoRR abs/2012.08816 (2020) - [i8]Anastasia Gaydashenko, Daniel Kudenko, Aleksei Shpilman:
A comparative evaluation of machine learning methods for robot navigation through human crowds. CoRR abs/2012.08822 (2020) - [i7]Aleksandra Malysheva, Daniel Kudenko, Aleksei Shpilman:
Learning to Run with Potential-Based Reward Shaping and Demonstrations from Video Data. CoRR abs/2012.08824 (2020) - [i6]Aleksandra Malysheva, Daniel Kudenko, Aleksei Shpilman:
MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning. CoRR abs/2012.09762 (2020) - [i5]Vladislav Belyaev, Aleksandra Malysheva, Aleksei Shpilman:
End-to-end Deep Object Tracking with Circular Loss Function for Rotated Bounding Box. CoRR abs/2012.09771 (2020) - [i4]Mikita Sazanovich, Anastasiya Nikolskaya, Yury Belousov
Solving Black-Box Optimization Challenge via Learning Search Space Partition for Local Bayesian Optimization. CoRR abs/2012.10335 (2020) - [i3]Aleksei Shpilman, Dmitry Boikiy, Marina Polyakova, Daniel Kudenko, Anton Burakov, Elena Nadezhdina:
Deep Learning of Cell Classification using Microscope Images of Intracellular Microtubule Networks. CoRR abs/2012.12125 (2020)
2010 – 2019
- 2019
- [c9]Artyom Lobanov, Timofey Bryksin
Automatic Classification of Error Types in Solutions to Programming Assignments at Online Learning Platform. AIED (2) 2019: 174-178 - [c8]Vladislav Belyaev, Aleksandra Malysheva, Aleksei Shpilman:
End-to-end Deep Object Tracking with Circular Loss Function for Rotated Bounding Box. REDUNDANCY 2019: 165-170 - [c7]Aleksandra Malysheva, Daniel Kudenko, Aleksei Shpilman:
MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning. REDUNDANCY 2019: 171-176 - [i2]Lukasz Kidzinski, Carmichael F. Ong, Sharada Prasanna Mohanty, Jennifer L. Hicks, Sean F. Carroll, Bo Zhou, Hong-cheng Zeng, Fan Wang, Rongzhong Lian, Hao Tian, Wojciech Jaskowski, Garrett Andersen, Odd Rune Lykkebø, Nihat Engin Toklu, Pranav Shyam, Rupesh Kumar Srivastava, Sergey Kolesnikov, Oleksii Hrinchuk, Anton Pechenko, Mattias Ljungström, Zhen Wang, Xu Hu, Zehong Hu, Minghui Qiu, Jun Huang, Aleksei Shpilman, Ivan Sosin, Oleg Svidchenko, Aleksandra Malysheva, Daniel Kudenko, Lance Rane, Aditya Bhatt, Zhengfei Wang, Penghui Qi, Zeyang Yu, Peng Peng, Quan Yuan, Wenxin Li, Yunsheng Tian, Ruihan Yang, Pingchuan Ma, Shauharda Khadka, Somdeb Majumdar, Zach Dwiel, Yinyin Liu, Evren Tumer, Jeremy D. Watson, Marcel Salathé, Sergey Levine, Scott L. Delp:
Artificial Intelligence for Prosthetics - challenge solutions. CoRR abs/1902.02441 (2019) - 2018
- [c6]Timofey Bryksin, Alexey Shpilman, Daniel Kudenko:
Automated Refactoring of Object-Oriented Code Using Clustering Ensembles. AAAI Workshops 2018: 754-757 - [c5]Aleksandra Malysheva, Daniel Kudenko, Aleksei Shpilman:
Learning to Run with Potential-Based Reward Shaping and Demonstrations from Video Data. ICARCV 2018: 286-291 - [c4]Ivan Sosin, Daniel Kudenko, Aleksei Shpilman:
Continuous Gesture Recognition from sEMG Sensor Data with Recurrent Neural Networks and Adversarial Domain Adaptation. ICARCV 2018: 1436-1441 - [c3]Anastasia Gaydashenko, Daniel Kudenko, Aleksei Shpilman:
A Comparative Evaluation of Machine Learning Methods for Robot Navigation Through Human Crowds. ICMLA 2018: 553-557 - [c2]Timofey Bryksin
Automatic recommendation of move method refactorings using clustering ensembles. IWoR@ASE 2018: 42-45 - [i1]Aleksandra Malysheva, Tegg Tae Kyong Sung, Chae-Bong Sohn, Daniel Kudenko, Aleksei Shpilman:
Deep Multi-Agent Reinforcement Learning with Relevance Graphs. CoRR abs/1811.12557 (2018) - 2017
- [c1]Aleksei Shpilman, Dmitry Boikiy, Marina Polyakova, Daniel Kudenko, Anton Burakov, Elena Nadezhdina:
Deep Learning of Cell Classification Using Microscope Images of Intracellular Microtubule Networks. ICMLA 2017: 1-6
Coauthor Index
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