623 citations · 738 across the 15 of their papers we have counts for
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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…
FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning
Marvin Alles, Nutan Chen, Patrick van der Smagt +1
The use of guidance to steer sampling toward desired outcomes has been widely explored within diffusion models, especially in applications such as image and trajectory generation.…
Local Distance Preserving Auto-encoders using Continuous k-Nearest Neighbours Graphs
Nutan Chen, Patrick van der Smagt, Botond Cseke
Auto-encoder models that preserve similarities in the data are a popular tool in representation learning. In this paper we introduce several auto-encoder models that preserve local…
Properties of Bethe Free Energies and Message Passing in Gaussian Models
Botond Cseke, Tom Heskes
We address the problem of computing approximate marginals in Gaussian probabilistic models by using mean field and fractional Bethe approximations. We define the Gaussian fractiona…
Bounds on the Bethe Free Energy for Gaussian Networks
Botond Cseke, Tom Heskes
We address the problem of computing approximate marginals in Gaussian probabilistic models by using mean field and fractional Bethe approximations. As an extension of Welling and T…