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Found 8 papers, 1 papers with code
Latent Matters: Learning Deep State-Space Models
Our results show that the constrained optimisation framework significantly improves system identification and prediction accuracy on the example of established state-of-the-art DSSMs.
Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models
Amortised inference enables scalable learning of sequential latent-variable models (LVMs) with the evidence lower bound (ELBO).
Variational Tracking and Prediction with Generative Disentangled State-Space Models
We address tracking and prediction of multiple moving objects in visual data streams as inference and sampling in a disentangled latent state-space model.
On Deep Set Learning and the Choice of Aggregations
Recently, it has been shown that many functions on sets can be represented by sum decompositions.
Approximate Bayesian inference in spatial environments
Model-based approaches bear great promise for decision making of agents interacting with the physical world.
Unsupervised Real-Time Control through Variational Empowerment
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.
Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data
We introduce Deep Variational Bayes Filters (DVBF), a new method for unsupervised learning and identification of latent Markovian state space models.
Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series
Approximate variational inference has shown to be a powerful tool for modeling unknown complex probability distributions.
