12 citations · 16 across the 7 of their papers we have counts for
7 papers
Reinforcement Learning for Assignment problem
Filipp Skomorokhov, George Ovchinnikov
This paper is dedicated to the application of reinforcement learning combined with neural networks to the general formulation of user scheduling problem. Our simulator resembles re…
How to optimize preconditioners for the conjugate gradient method: a stochastic approach
Alexandr Katrutsa, Mike Botchev, George Ovchinnikov +1
The conjugate gradient method (CG) is typically used with a preconditioner which improves efficiency and robustness of the method. Many preconditioners include parameters and a pro…
Data-driven model for the identification of the rock type at a drilling bit
Nikita Klyuchnikov, Alexey Zaytsev, Arseniy Gruzdev +12
Directional oil well drilling requires high precision of the wellbore positioning inside the productive area. However, due to specifics of engineering design, sensors that explicit…
AA-ICP: Iterative Closest Point with Anderson Acceleration
A. L. Pavlov, G. V. Ovchinnikov, D. Yu. Derbyshev +2
Iterative Closest Point (ICP) is a widely used method for performing scan-matching and registration. Being simple and robust method, it is still computationally expensive and may b…
Linear complexity SimRank computation based on the iterative diagonal estimation
I. V. Oseledets, G. V. Ovchinnikov, A. M. Katrutsa
This paper presents a deterministic linear time complexity IDE-SimRank method to approximately compute SimRank with proved error bound. SimRank is a well-known similarity measure b…
Algebraic reputation model RepRank and its application to spambot detection
G. V. Ovchinnikov, D. A. Kolesnikov, I. V. Oseledets
Due to popularity surge social networks became lucrative targets for spammers and guerilla marketers, who are trying to game ranking systems and broadcast their messages at little…
Fast, memory efficient low-rank approximation of SimRank
I. V. Oseledets, G. V. Ovchinnikov
SimRank is a well-known similarity measure between graph vertices. In this paper novel low-rank approximation of SimRank is proposed.