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Found 5 papers, 4 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.
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.
Variational Dropout Sparsification for Particle Identification speed-up
Accurate particle identification (PID) is one of the most important aspects of the LHCb experiment.
NFAD: Fixing anomaly detection using normalizing flows
Most of the conventional approaches to anomaly detection, such as one-class SVM and Robust Auto-Encoder, are one-class classification methods, i. e. focus on separating normal data from the rest of the space.
$(1 + \varepsilon)$-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets
We explore a novel method that gives a trade-off possibility between one-class and two-class approaches, and leads to a better performance on anomaly detection problems with small or non-representative anomalous samples.
