35 citations · 140 across the 24 of their papers we have counts for
10 papers · 1 filter
End-to-end Deep Object Tracking with Circular Loss Function for Rotated Bounding Box
Vladislav Belyaev, Aleksandra Malysheva, Aleksei Shpilman
The task object tracking is vital in numerous applications such as autonomous driving, intelligent surveillance, robotics, etc. This task entails the assigning of a bounding box to…
MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning
Aleksandra Malysheva, Daniel Kudenko, Aleksei Shpilman
Over recent years, deep reinforcement learning has shown strong successes in complex single-agent tasks, and more recently this approach has also been applied to multi-agent domain…
Deep Learning of Cell Classification using Microscope Images of Intracellular Microtubule Networks
Aleksei Shpilman, Dmitry Boikiy, Marina Polyakova +3
Microtubule networks (MTs) are a component of a cell that may indicate the presence of various chemical compounds and can be used to recognize properties such as treatment resistan…
Learning to Run with Potential-Based Reward Shaping and Demonstrations from Video Data
Aleksandra Malysheva, Daniel Kudenko, Aleksei Shpilman
Learning to produce efficient movement behaviour for humanoid robots from scratch is a hard problem, as has been illustrated by the "Learning to run" competition at NIPS 2017. The…
A comparative evaluation of machine learning methods for robot navigation through human crowds
Anastasia Gaydashenko, Daniel Kudenko, Aleksei Shpilman
Robot navigation through crowds poses a difficult challenge to AI systems, since the methods should result in fast and efficient movement but at the same time are not allowed to co…
Continuous Gesture Recognition from sEMG Sensor Data with Recurrent Neural Networks and Adversarial Domain Adaptation
Ivan Sosin, Daniel Kudenko, Aleksei Shpilman
Movement control of artificial limbs has made big advances in recent years. New sensor and control technology enhanced the functionality and usefulness of artificial limbs to the p…
Solving Black-Box Optimization Challenge via Learning Search Space Partition for Local Bayesian Optimization
Mikita Sazanovich, Anastasiya Nikolskaya, Yury Belousov +1
Black-box optimization is one of the vital tasks in machine learning, since it approximates real-world conditions, in that we do not always know all the properties of a given syste…
Lipophilicity Prediction with Multitask Learning and Molecular Substructures Representation
Nina Lukashina, Alisa Alenicheva, Elizaveta Vlasova +5
Lipophilicity is one of the factors determining the permeability of the cell membrane to a drug molecule. Hence, accurate lipophilicity prediction is an essential step in the devel…
Automatic generation of reviews of scientific papers
Anna Nikiforovskaya, Nikolai Kapralov, Anna Vlasova +2
With an ever-increasing number of scientific papers published each year, it becomes more difficult for researchers to explore a field that they are not closely familiar with alread…
Imitation Learning Approach for AI Driving Olympics Trained on Real-world and Simulation Data Simultaneously
Mikita Sazanovich, Konstantin Chaika, Kirill Krinkin +1
In this paper, we describe our winning approach to solving the Lane Following Challenge at the AI Driving Olympics Competition through imitation learning on a mixed set of simulati…