3 citations · 7 across the 6 of their papers we have counts for
7 papers
Entropy-Based Characterisation of the Polarised Regime in Latent Variable Models
Peter Clapham, Lisa Bonheme, Marek Grzes
Variational Autoencoders (VAEs) often exhibit a polarised regime in which latent variables separate into active, passive, and mixed subsets. Existing criteria for identifying activ…
aedFaCT: Scientific Fact-Checking Made Easier via Semi-Automatic Discovery of Relevant Expert Opinions
Enes Altuncu, Jason R. C. Nurse, Meryem Bagriacik +4
In this highly digitised world, fake news is a challenging problem that can cause serious harm to society. Considering how fast fake news can spread, automated methods, tools and s…
How good are variational autoencoders at transfer learning?
Lisa Bonheme, Marek Grzes
Variational autoencoders (VAEs) are used for transfer learning across various research domains such as music generation or medical image analysis. However, there is no principled w…
Deconstructing deep active inference
Théophile Champion, Marek Grześ, Lisa Bonheme +1
Active inference is a theory of perception, learning and decision making, which can be applied to neuroscience, robotics, and machine learning. Recently, reasearch has been taking…
FONDUE: an algorithm to find the optimal dimensionality of the latent representations of variational autoencoders
Lisa Bonheme, Marek Grzes
When training a variational autoencoder (VAE) on a given dataset, determining the optimal number of latent variables is mostly done by grid search: a costly process in terms of com…
How do Variational Autoencoders Learn? Insights from Representational Similarity
Lisa Bonheme, Marek Grzes
The ability of Variational Autoencoders (VAEs) to learn disentangled representations has made them popular for practical applications. However, their behaviour is not yet fully und…
Be More Active! Understanding the Differences between Mean and Sampled Representations of Variational Autoencoders
Lisa Bonheme, Marek Grzes
The ability of Variational Autoencoders to learn disentangled representations has made them appealing for practical applications. However, their mean representations, which are gen…