604 citations · 679 across the 18 of their papers we have counts for
14 papers · 1 filter
Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF
Atanas Mirchev, Baris Kayalibay, Patrick van der Smagt +1
We solve the problem of 6-DoF localisation and 3D dense reconstruction in spatial environments as approximate Bayesian inference in a deep state-space model. Our approach leverages…
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…
Variational Tracking and Prediction with Generative Disentangled State-Space Models
Adnan Akhundov, Maximilian Soelch, Justin Bayer +1
We address tracking and prediction of multiple moving objects in visual data streams as inference and sampling in a disentangled latent state-space model. By encoding objects separ…
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…
Switching Linear Dynamics for Variational Bayes Filtering
Philip Becker-Ehmck, Jan Peters, Patrick van der Smagt
System identification of complex and nonlinear systems is a central problem for model predictive control and model-based reinforcement learning. Despite their complexity, such syst…
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…