no code implementations • 7 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.
1 code implementation • 17 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.
2 code implementations • 11 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.
1 code implementation • 9 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.
2 code implementations • Data Mining and Knowledge Discovery 2019 • Gerrit J. J. van den Burg, Alfredo Nazabal, Charles Sutton
Existing dialect detection approaches are few and non-robust.
Databases E.5; H.2.8
2 code implementations • 10 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.