11 citations · 23 across the 4 of their papers we have counts for
4 papers
Latent Matters: Learning Deep State-Space Models
Alexej Klushyn, Richard Kurle, Maximilian Soelch +2
Deep state-space models (DSSMs) enable temporal predictions by learning the underlying dynamics of observed sequence data. They are often trained by maximising the evidence lower b…
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.…