74 citations · 126 across the 4 of their papers we have counts for
3 papers · 1 filter
On Deep Set Learning and the Choice of Aggregations
Maximilian Soelch, Adnan Akhundov, Patrick van der Smagt +1
Recently, it has been shown that many functions on sets can be represented by sum decompositions. These decompositons easily lend themselves to neural approximations, extending the…
Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data
Maximilian Karl, Maximilian Soelch, Justin Bayer +1
We introduce Deep Variational Bayes Filters (DVBF), a new method for unsupervised learning and identification of latent Markovian state space models. Leveraging recent advances in…
Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series
Maximilian Soelch, Justin Bayer, Marvin Ludersdorfer +1
Approximate variational inference has shown to be a powerful tool for modeling unknown complex probability distributions. Recent advances in the field allow us to learn probabilist…