604 citations · 732 across the 38 of their papers we have counts for
8 papers · 1 filter
Beta DVBF: Learning State-Space Models for Control from High Dimensional Observations
Neha Das, Maximilian Karl, Philip Becker-Ehmck +1
Learning a model of dynamics from high-dimensional images can be a core ingredient for success in many applications across different domains, especially in sequential decision maki…
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…
Estimating Fingertip Forces, Torques, and Local Curvatures from Fingernail Images
Nutan Chen, Göran Westling, Benoni B. Edin +1
The study of dexterous manipulation has provided important insights in humans sensorimotor control as well as inspiration for manipulation strategies in robotic hands. Previous wor…
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…