A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Filters
NEU: A Meta-Algorithm for Universal UAP-Invariant Feature Representation
[article]
2021
arXiv
pre-print
Preserved Fulltext
Other Versions
This paper introduces a meta-procedure, called Non-Euclidean Upgrading (NEU), which learns feature maps that are expressive enough to embed the universal approximation property (UAP) into most model classes ...
Effective feature representation is key to the predictive performance of any algorithm. ...
The authors thank Alina Stancu (Concordia University) for helpful discussions, Josef Teichmann and the entire working group at ETH for their helpful feedback, and Behnoosh Zamanlooy for the Python guidance ...
arXiv:1809.00082v4
fatcat:5ue5d7svp5dyhedo7er65ou7cm
Probabilistic Feature Selection in Joint Quantile Time Series Analysis
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Other Versions
In this paper, we propose a general probabilistic methodology for feature selection in joint quantile time series analysis, under the name of quantile feature selection time series (QFSTS) model. ...
Quantile feature selection over correlated multivariate time series data has always been a methodological challenge and is an open problem. ...
Neu: A meta-algorithm for universal
uap-invariant feature representation. ...
arXiv:2010.01654v2
fatcat:o5bhgjstejgatpcnwjchpflrvq