-
Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models
Amortised inference enables scalable learning of sequential latent-varia...
01/18/2021 ∙ by Justin Bayer, et al. ∙
0
∙
share
read it
-
Variational Tracking and Prediction with Generative Disentangled State-Space Models
We address tracking and prediction of multiple moving objects in visual ...
10/14/2019 ∙ by Adnan Akhundov, et al. ∙
8
∙
share
read it
-
On Deep Set Learning and the Choice of Aggregations
Recently, it has been shown that many functions on sets can be represent...
03/18/2019 ∙ by Maximilian Soelch, et al. ∙
4
∙
share
read it
-
Unsupervised Real-Time Control through Variational Empowerment
We introduce a methodology for efficiently computing a lower bound to em...
10/13/2017 ∙ by Maximilian Karl, et al. ∙
0
∙
share
read it
-
Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data
We introduce Deep Variational Bayes Filters (DVBF), a new method for uns...
05/20/2016 ∙ by Maximilian Karl, et al. ∙
0
∙
share
read it
-
Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series
Approximate variational inference has shown to be a powerful tool for mo...
02/23/2016 ∙ by Maximilian Soelch, et al. ∙
0
∙
share
read it
