This is the final project of the course 10-708 Probablistic Graphical Models at Carnegie Mellon University. It studies the use of Restricted Boltzmann Machines, Deep Belief Network and Variational AutoEncoders to estimate Origin-Destination matrices. The analyses were performed using synthetic data and the models were implemented with TensorFlow primarily.
- Clone the repository
- Create virtual environment (e.g. "venv")
python3 -m venv venv - Activate virtual environment
source venv/bin/activate - Install the development dependencies:
pip install -r requirements.txt - Run
python3 main.py
