A PyTorch implementation of a Deep Hidden Markov Model: Structured Inference Networks for Nonlinear State Space Models.
Adopted from https://github.com/uber/pyro/tree/dev/examples/dmm
and https://github.com/clinicalml/structuredinference
- observations (for dataset)
https://github.com/clinicalml/theanomodels/blob/master/datasets/synthp.py
- Structured Inference Networks for Nonlinear State Space Models
- 2019ICLR - A NOVEL VARIATIONAL FAMILY FOR HIDDEN NON-LINEAR MARKOV MODELS
- A Recurrent Latent Variable Model for Sequential Data
- STATE SPACE LSTM MODELS WITH PARTICLE MCMC INFERENCE
- BLACK BOX VARIATIONAL INFERENCE FOR STATE SPACE MODEL
- Gaussian variational approximation for high-dimensional state space models
- Generating Long-term Trajectories Using Deep Hierarchical Networks
- Disentangled Sequential Autoencoder