8 citations · 8 across the 1 of their papers we have counts for
4 papers
VAEs in the Presence of Missing Data
Mark Collier, Alfredo Nazabal, Christopher K. I. Williams
Real world datasets often contain entries with missing elements e.g. in a medical dataset, a patient is unlikely to have taken all possible diagnostic tests. Variational Autoencode…
Data Engineering for Data Analytics: A Classification of the Issues, and Case Studies
Alfredo Nazabal, Christopher K. I. Williams, Giovanni Colavizza +2
Consider the situation where a data analyst wishes to carry out an analysis on a given dataset. It is widely recognized that most of the analyst's time will be taken up with \emph{…
Robust Variational Autoencoders for Outlier Detection and Repair of Mixed-Type Data
Simão Eduardo, Alfredo Nazábal, Christopher K. I. Williams +1
We focus on the problem of unsupervised cell outlier detection and repair in mixed-type tabular data. Traditional methods are concerned only with detecting which rows in the datase…
Handling Incomplete Heterogeneous Data using VAEs
Alfredo Nazabal, Pablo M. Olmos, Zoubin Ghahramani +1
Variational autoencoders (VAEs), as well as other generative models, have been shown to be efficient and accurate for capturing the latent structure of vast amounts of complex high…