Adversarially Trained Restricted Boltzmann Machines

This respository contains an implementations of Gaussian-Bernoulli RBMs and conditional RBMs described in our term paper Adversarially Trained Restricted Boltzmann Machines for the course CSCI 2952Q taught by Professor Yu Cheng at Brown University.

Key References: Papers

1. Boltzmann Encoded Adversarial Machines (Fisher et al 2018)
2. Gaussian Bernoulli RBMs Without Tears (Liao et al 2022)
3. Modeling Human Motion Using Binary Latent Variables (Taylor et al 2006)

Please check out our term paper for a complete list of references.

Key References: Code

The following GitHub repositories were used as reference in the developement of the code presented in this repository.

1. GRBM (from Gaussian Bernoulli RBMs Without Tears, Liao et al 2022)
2. CRBM (from Modeling Human Motion Using Binary Latent Variables, Taylor et al 2006)

Dependencies

1. Python >= 3.11.5
2. PyTorch >= 2.1.1
3. NumPy >= 1.24.3
4. SciPy >= 1.11.1
5. scikit-learn >= 1.3.0