10 citations · 26 across the 6 of their papers we have counts for
7 papers
Inference and Learning for Generative Capsule Models
Alfredo Nazabal, Nikolaos Tsagkas, Christopher K. I. Williams
Capsule networks (see e.g. Hinton et al., 2018) aim to encode knowledge of and reason about the relationship between an object and its parts. In this paper we specify a generative…
Repairing Systematic Outliers by Learning Clean Subspaces in VAEs
Simao Eduardo, Kai Xu, Alfredo Nazabal +1
Data cleaning often comprises outlier detection and data repair. Systematic errors result from nearly deterministic transformations that occur repeatedly in the data, e.g. specific…
Inference for Generative Capsule Models
Alfredo Nazabal, Nikolaos Tsagkas, Christopher K. I. Williams
Capsule networks (see e.g. Hinton et al., 2018) aim to encode knowledge and reason about the relationship between an object and its parts. In this paper we specify a \emph{generati…
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…