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Found 2 papers, 1 papers with code
Latent Optimal Paths by Gumbel Propagation for Variational Bayesian Dynamic Programming
We show the equivalence of the Gibbs distribution to a message-passing algorithm by the properties of the Gumbel distribution and give all the ingredients required for variational Bayesian inference of a latent path, namely Bayesian dynamic programming (BDP).
How do Mixture Density RNNs Predict the Future?
Gaining a better understanding of how and what machine learning systems learn is important to increase confidence in their decisions and catalyze further research.
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