Andrey Ustyuzhanin | DeepAI

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∙ 01/15/2023

Symbolic expression generation via Variational Auto-Encoder

There are many problems in physics, biology, and other natural sciences ...

0 Sergei Popov, et al. ∙

∙ 10/28/2022

Code4ML: a Large-scale Dataset of annotated Machine Learning Code

Program code as a data source is gaining popularity in the data science ...

0 Anastasia Drozdova, et al. ∙

∙ 05/03/2021

The Tracking Machine Learning challenge : Throughput phase

This paper reports on the second "Throughput" phase of the Tracking Mach...

0 Sabrina Amrouche, et al. ∙

∙ 04/05/2021

Segmentation of EM showers for neutrino experiments with deep graph neural networks

We introduce a novel method for showers reconstruction from the data col...

0 Vladislav Belavin, et al. ∙

∙ 01/18/2021

Online detection of failures generated by storage simulator

Modern large-scale data-farms consist of hundreds of thousands of storag...

0 Kenenbek Arzymatov, et al. ∙

∙ 07/08/2020

A study of Neural networks point source extraction on simulated Fermi/LAT Telescope images

Astrophysical images in the GeV band are challenging to analyze due to t...

5 Mariia Drozdova, et al. ∙

∙ 02/11/2020

Differentiating the Black-Box: Optimization with Local Generative Surrogates

We propose a novel method for gradient-based optimization of black-box s...

22 Sergey Shirobokov, et al. ∙

∙ 01/17/2020

Generalization of Change-Point Detection in Time Series Data Based on Direct Density Ratio Estimation

The goal of the change-point detection is to discover changes of time se...

1 Mikhail Hushchyn, et al. ∙

∙ 12/19/2019

Normalizing flows for deep anomaly detection

Anomaly detection for complex data is a challenging task from the perspe...

31 Artem Ryzhikov, et al. ∙

∙ 12/01/2019

Adaptive Divergence for Rapid Adversarial Optimization

Adversarial Optimization (AO) provides a reliable, practical way to matc...

0 Maxim Borisyak, et al. ∙

∙ 06/14/2019

(1 + ε)-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets

Anomaly detection is not an easy problem since distribution of anomalous...

33 Maxim Borisyak, et al. ∙

∙ 05/28/2019

Fast Data-Driven Simulation of Cherenkov Detectors Using Generative Adversarial Networks

The increasing luminosities of future Large Hadron Collider runs and nex...

0 Artem Maevskiy, et al. ∙

∙ 03/28/2019

Cherenkov Detectors Fast Simulation Using Neural Networks

We propose a way to simulate Cherenkov detector response using a generat...

0 Denis Derkach, et al. ∙

∙ 02/06/2019

Space Navigator: a Tool for the Optimization of Collision Avoidance Maneuvers

The number of space objects will grow several times in a few years due t...

0 Leonid Gremyachikh, et al. ∙

∙ 12/04/2018

Generative Models for Fast Calorimeter Simulation.LHCb case

Simulation is one of the key components in high energy physics. Historic...

0 Viktoria Chekalina, et al. ∙

∙ 07/08/2018

Machine Learning in High Energy Physics Community White Paper

Machine learning is an important research area in particle physics, begi...

0 Kim Albertsson, et al. ∙

∙ 09/25/2017

Numerical optimization for Artificial Retina Algorithm

High-energy physics experiments rely on reconstruction of the trajectori...

0 Maxim Borisyak, et al. ∙

∙ 09/25/2017

Towards automation of data quality system for CERN CMS experiment

Daily operation of a large-scale experiment is a challenging task, parti...

0 Maxim Borisyak, et al. ∙

∙ 09/25/2017

Muon Trigger for Mobile Phones

The CRAYFIS experiment proposes to use privately owned mobile phones as ...

0 Maxim Borisyak, et al. ∙

∙ 07/27/2015

A genetic algorithm for autonomous navigation in partially observable domain

The problem of autonomous navigation is one of the basic problems for ro...

0 Maxim Borisyak, et al. ∙

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