Search Results for author:
Found 11 papers, 8 papers with code
Performance Modeling of Data Storage Systems using Generative Models
The results of the experiments demonstrate the errors of 4-10 % for IOPS and 3-16 % for latency predictions depending on the components and models of the system.
Understanding of the properties of neural network approaches for transient light curve approximations
Modern-day time-domain photometric surveys collect a lot of observations of various astronomical objects and the coming era of large-scale surveys will provide even more information on their properties.
Latent Neural Stochastic Differential Equations for Change Point Detection
Change point detection algorithms are aimed to locating abrupt changes in the time series behaviour of a process.
Supernova Light Curves Approximation based on Neural Network Models
Normalizing Flows exceeds Gaussian processes in terms of approximation quality as well.
The Tracking Machine Learning challenge : Throughput phase
Both were measured on the Codalab platform where the participants had to upload their software.
Online detection of failures generated by storage simulator
In this work, we challenge two problems: 1) lack of storage data in the methods above by creating a simulator and 2) applying existing online algorithms that can faster detect a failure occurred in one of the components.
Online Neural Networks for Change-Point Detection
Moments when a time series changes its behaviour are called change points.
Photometric Data-driven Classification of Type Ia Supernovae in the Open Supernova Catalog
We propose a novel approach for a machine-learning-based detection of the type Ia supernovae using photometric information.
Variational Dropout Sparsification for Particle Identification speed-up
Accurate particle identification (PID) is one of the most important aspects of the LHCb experiment.
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 series distribution.
Disk storage management for LHCb based on Data Popularity estimator
We use regression algorithms and time series analysis to find the optimal number of replicas for datasets that are kept on disk.
