clinicalml/dmm
Deep Markov Models
This project helps machine learning researchers implement Deep Markov Models. It takes time-varying observational data, like polyphonic music or other sequential measurements, and generates a model of the underlying hidden states. Researchers can then use this model to understand the generative process of their data or to make predictions. This is for researchers specializing in deep learning and probabilistic modeling.
132 stars. No commits in the last 6 months.
Use this if you are a machine learning researcher working with time-series or sequential data and need to model complex, non-linear dependencies between observations and hidden states.
Not ideal if you are a practitioner looking for a ready-to-use solution for standard time-series forecasting or anomaly detection without deep learning expertise.
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132
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35
Language
Jupyter Notebook
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Last pushed
Apr 28, 2019
Commits (30d)
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