Search Results for author: Alfredo Nazabal

Found 6 papers, 4 papers with code

Inference and Learning for Generative Capsule Models

no code implementations7 Sep 2022 Alfredo Nazabal, Nikolaos Tsagkas, Christopher K. I. Williams

In this paper we specify a generative model for such data, and derive a variational algorithm for inferring the transformation of each model object in a scene, and the assignments of observed parts to the objects.

Repairing Systematic Outliers by Learning Clean Subspaces in VAEs

no code implementations17 Jul 2022 Simao Eduardo, Kai Xu, Alfredo Nazabal, Charles Sutton

Seeing as a systematic outlier is a combination of patterns of a clean instance and systematic error patterns, our main insight is that inliers can be modelled by a smaller representation (subspace) in a model than outliers.

Outlier Detection

Inference for Generative Capsule Models

2 code implementations11 Mar 2021 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.

VAEs in the Presence of Missing Data

1 code implementation9 Jun 2020 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.

Imputation Missing Elements

Handling Incomplete Heterogeneous Data using VAEs

2 code implementations10 Jul 2018 Alfredo Nazabal, Pablo M. Olmos, Zoubin Ghahramani, Isabel Valera

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-dimensional data.


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