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Found 4 papers, 1 papers with code
Active Learning for Computationally Efficient Distribution of Binary Evolution Simulations
We find that optimizing for classification only may lead to performance losses in regression, and vice versa.
Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO
Probabilistic methods, such as Gaussian Processes (GP), have proven successful in modeling this setting.
DIRECT: Deep Discriminative Embedding for Clustering of LIGO Data
In this paper, benefiting from the strong ability of deep neural network in estimating non-linear functions, we propose a discriminative embedding function to be used as a feature extractor for clustering tasks.
Deep Multi-view Models for Glitch Classification
Non-cosmic, non-Gaussian disturbances known as "glitches", show up in gravitational-wave data of the Advanced Laser Interferometer Gravitational-wave Observatory, or aLIGO.
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