11 citations · 15 across the 3 of their papers we have counts for
3 papers
Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models
Justin Bayer, Maximilian Soelch, Atanas Mirchev +2
Amortised inference enables scalable learning of sequential latent-variable models (LVMs) with the evidence lower bound (ELBO). In this setting, variational posteriors are often on…
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…
Unsupervised Real-Time Control through Variational Empowerment
Maximilian Karl, Maximilian Soelch, Philip Becker-Ehmck +3
We introduce a methodology for efficiently computing a lower bound to empowerment, allowing it to be used as an unsupervised cost function for policy learning in real-time control.…