3 citations · 4 across the 2 of their papers we have counts for
5 papers · 1 filter
Learning Flat Latent Manifolds with VAEs
Nutan Chen, Alexej Klushyn, Francesco Ferroni +2
Measuring the similarity between data points often requires domain knowledge, which can in parts be compensated by relying on unsupervised methods such as latent-variable models, w…
Increasing the Generalisation Capacity of Conditional VAEs
Alexej Klushyn, Nutan Chen, Botond Cseke +2
We address the problem of one-to-many mappings in supervised learning, where a single instance has many different solutions of possibly equal cost. The framework of conditional var…
Learning Hierarchical Priors in VAEs
Alexej Klushyn, Nutan Chen, Richard Kurle +2
We propose to learn a hierarchical prior in the context of variational autoencoders to avoid the over-regularisation resulting from a standard normal prior distribution. To incenti…
Fast Approximate Geodesics for Deep Generative Models
Nutan Chen, Francesco Ferroni, Alexej Klushyn +3
The length of the geodesic between two data points along a Riemannian manifold, induced by a deep generative model, yields a principled measure of similarity. Current approaches ar…
Active Learning based on Data Uncertainty and Model Sensitivity
Nutan Chen, Alexej Klushyn, Alexandros Paraschos +2
Robots can rapidly acquire new skills from demonstrations. However, during generalisation of skills or transitioning across fundamentally different skills, it is unclear whether th…