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Thurstonian Boltzmann Machines: Learning from Multiple Inequalities
[article]
2014
arXiv
pre-print
Preserved Fulltext
We introduce Thurstonian Boltzmann Machines (TBM), a unified architecture that can naturally incorporate a wide range of data inputs at the same time. ...
Thus learning and inference in TBM reduce to making sense of a set of inequalities. ...
Thurstonian Boltzmann Machines We now generalise the Gaussian RBM into the Thurstonian Boltzmann Machine (TBM). Denote by e an observed evidence of x. ...
arXiv:1408.0055v1
fatcat:4y3vqsvwlrb4jjtoovk7yxxcc4
Estimation of Skill Distributions
[article]
2020
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 2020; you can also visit the original URL.
The file type is application/pdf.
The problem is, in essence, to learn a distribution from noisy, quantized observations. ...
Our approach brings together prior work on learning skill parameters from pairwise comparisons with kernel density estimation from non-parametric statistics. ...
a modern machine learning perspective. ...
arXiv:2006.08189v1
fatcat:5zeipcxr5rg7lfvok7cep55qva