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Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models
Amortised inference enables scalable learning of sequential latent-varia...
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Variational Tracking and Prediction with Generative Disentangled State-Space Models
We address tracking and prediction of multiple moving objects in visual ...
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On Deep Set Learning and the Choice of Aggregations
Recently, it has been shown that many functions on sets can be represent...
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Unsupervised Real-Time Control through Variational Empowerment
We introduce a methodology for efficiently computing a lower bound to em...
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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...
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Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series
Approximate variational inference has shown to be a powerful tool for mo...
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Maximilian Soelch
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