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A PyTorch implementation of a Deep Hidden Markov Model [Structured Inference Networks for Nonlinear State Space Models]

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  - test.pkl
  - train.pkl
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- - __init__.cpython-36.pyc
  - dhmm.cpython-36.pyc
  - dmm.cpython-36.pyc
  - gan_dhmm.cpython-36.pyc
- __init__.py
- dhmm.py
configs.py
data_loader.py
helper.py
modules.py
prepare_data.py
README.md
requirements.txt
train.py

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# DHMM
A PyTorch implementation of a Deep Hidden Markov Model: [Structured Inference Networks for Nonlinear State Space Models](https://arxiv.org/pdf/1609.09869.pdf).

Adopted from https://github.com/uber/pyro/tree/dev/examples/dmm  
         and https://github.com/clinicalml/structuredinference

## Dependency

- observations (for dataset)

## Dataset

### Synthetic
https://github.com/clinicalml/theanomodels/blob/master/datasets/synthp.py
### Polyphonic

## Related Work

- [Structured Inference Networks for Nonlinear State Space Models](https://arxiv.org/pdf/1609.09869.pdf)
- [2019ICLR - A NOVEL VARIATIONAL FAMILY FOR HIDDEN NON-LINEAR MARKOV MODELS](https://openreview.net/pdf?id=SJMO2iCct7)
- [A Recurrent Latent Variable Model for Sequential Data](https://arxiv.org/pdf/1506.02216.pdf)
- [STATE SPACE LSTM MODELS WITH PARTICLE MCMC INFERENCE](https://arxiv.org/pdf/1711.11179.pdf)
- [BLACK BOX VARIATIONAL INFERENCE FOR STATE SPACE MODEL](Shttps://arxiv.org/pdf/1511.07367.pdf)
- [Gaussian variational approximation for high-dimensional state space models](https://arxiv.org/pdf/1801.07873.pdf)
- [Generating Long-term Trajectories Using Deep Hierarchical Networks](http://papers.nips.cc/paper/6520-generating-long-term-trajectories-using-deep-hierarchical-networks.pdf)
- [Disentangled Sequential Autoencoder](https://arxiv.org/pdf/1803.02991.pdf)

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