Anahita in our next lab meeting will talk about various ways of applying Neural Networks in Hodden Markov Models . here’s the abstract her of talk:
We present how Hidden Markov alignment model can be neuralized. In particular, we provide neural network-based emission and transition models. The standard forward-backward algorithm still applies to compute the posterior probabilities. We then backpropagate the posteriors through the networks to maximize the likelihood of the data.
Thursday, Nov. 23, 10-11 AM, Location: TASC1 9408.
