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Found 2 papers, 1 papers with code
Neural Likelihoods via Cumulative Distribution Functions
We leverage neural networks as universal approximators of monotonic functions to build a parameterization of conditional cumulative distribution functions (CDFs).
Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages
Factorial Hidden Markov Models (FHMMs) are powerful models for sequential data but they do not scale well with long sequences.
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