no code implementations • 28 Mar 2023 • Stefanos Eleftheriadis, Dominic Richards, James Hensman
Further, we introduce sparseness in the eigenbasis by variational learning of the spherical harmonic phases.
no code implementations • 15 Jan 2020 • Vincent Adam, Stefanos Eleftheriadis, Nicolas Durrande, Artem Artemev, James Hensman
The use of Gaussian process models is typically limited to datasets with a few tens of thousands of observations due to their complexity and memory footprint.
no code implementations • 26 Feb 2019 • Nicolas Durrande, Vincent Adam, Lucas Bordeaux, Stefanos Eleftheriadis, James Hensman
Banded matrices can be used as precision matrices in several models including linear state-space models, some Gaussian processes, and Gaussian Markov random fields.
no code implementations • 24 Mar 2018 • Hugh Salimbeni, Stefanos Eleftheriadis, James Hensman
The natural gradient method has been used effectively in conjugate Gaussian process models, but the non-conjugate case has been largely unexplored.
no code implementations • NeurIPS 2017 • Stefanos Eleftheriadis, Thomas F. W. Nicholson, Marc Peter Deisenroth, James Hensman
To address this challenge, we impose a structured Gaussian variational posterior distribution over the latent states, which is parameterised by a recognition model in the form of a bi-directional recurrent neural network.
no code implementations • ICCV 2017 • Dieu Linh Tran, Robert Walecki, Ognjen Rudovic, Stefanos Eleftheriadis, Bjørn Schuller, Maja Pantic
Potentially, this makes VAEs a suitable approach for learning facial features for AU intensity estimation.
no code implementations • 16 Aug 2016 • Stefanos Eleftheriadis, Ognjen Rudovic, Marc P. Deisenroth, Maja Pantic
In particular, we introduce GP encoders to project multiple observed features onto a latent space, while GP decoders are responsible for reconstructing the original features.
no code implementations • 11 Apr 2016 • Stefanos Eleftheriadis, Ognjen Rudovic, Marc P. Deisenroth, Maja Pantic
The adaptation of the classifier is facilitated in probabilistic fashion by conditioning the target expert on multiple source experts.
no code implementations • ICCV 2015 • Stefanos Eleftheriadis, Ognjen Rudovic, Maja Pantic
We propose a novel multi-conditional latent variable model for simultaneous facial feature fusion and detection of facial action units.