[HTML][HTML] Text classification algorithms: A survey
In recent years, there has been an exponential growth in the number of complex documents
and texts that require a deeper understanding of machine learning methods to be able to …
and texts that require a deeper understanding of machine learning methods to be able to …
A comprehensive survey on word representation models: From classical to state-of-the-art word representation language models
Word representation has always been an important research area in the history of natural
language processing (NLP). Understanding such complex text data is imperative, given that …
language processing (NLP). Understanding such complex text data is imperative, given that …
Understanding the lomb–scargle periodogram
JT VanderPlas - The Astrophysical Journal Supplement Series, 2018 - iopscience.iop.org
Abstract The Lomb–Scargle periodogram is a well-known algorithm for detecting and
characterizing periodic signals in unevenly sampled data. This paper presents a conceptual …
characterizing periodic signals in unevenly sampled data. This paper presents a conceptual …
Fast and scalable Gaussian process modeling with applications to astronomical time series
The growing field of large-scale time domain astronomy requires methods for probabilistic
data analysis that are computationally tractable, even with large data sets. Gaussian …
data analysis that are computationally tractable, even with large data sets. Gaussian …
[LIVRE][B] Statistics, data mining, and machine learning in astronomy: a practical Python guide for the analysis of survey data
As telescopes, detectors, and computers grow ever more powerful, the volume of data at the
disposal of astronomers and astrophysicists will enter the petabyte domain, providing …
disposal of astronomers and astrophysicists will enter the petabyte domain, providing …
Scalable backpropagation for Gaussian processes using celerite
D Foreman-Mackey - arxiv preprint arxiv:1801.10156, 2018 - arxiv.org
This research note presents a derivation and implementation of efficient and scalable
gradient computations using the celerite algorithm for Gaussian Process (GP) modeling. The …
gradient computations using the celerite algorithm for Gaussian Process (GP) modeling. The …
Vecchia approximations of Gaussian-process predictions
Gaussian processes (GPs) are highly flexible function estimators used for geospatial
analysis, nonparametric regression, and machine learning, but they are computationally …
analysis, nonparametric regression, and machine learning, but they are computationally …
A comparison of period finding algorithms
This paper presents a comparison of popular period finding algorithms applied to the light
curves of variable stars from the Catalina Real-Time Transient Survey, MACHO and ASAS …
curves of variable stars from the Catalina Real-Time Transient Survey, MACHO and ASAS …
Using conditional entropy to identify periodicity
This paper presents a new period-finding method based on conditional entropy that is both
efficient and accurate. We demonstrate its applicability on simulated and real data. We find …
efficient and accurate. We demonstrate its applicability on simulated and real data. We find …
Common dynamo scaling in slowly rotating young and evolved stars
One interpretation of the activity and magnetism of late-type stars is that these both intensify
with decreasing Rossby number up to a saturation level,–, suggesting that stellar dynamos …
with decreasing Rossby number up to a saturation level,–, suggesting that stellar dynamos …