Belief Net learns HMM parameters by implementing the forward filter as a decoder-only neural network whose weights are the logits of the initial, transition, and emission distributions, trained end-to-end with autoregressive loss.
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Differentiable Filtering for Learning Hidden Markov Models
Belief Net learns HMM parameters by implementing the forward filter as a decoder-only neural network whose weights are the logits of the initial, transition, and emission distributions, trained end-to-end with autoregressive loss.