A PyTorch implementation of Adaptive Latent Modeling and Optimization via Neural Networks and Langevin Diffusion (ALMOND)
- pytorch
- scipy
- sklearn
- pandas
- matplotlib
- tqdm
I was only interested in the exp, mix, and glmm (or copula) cases. So there are three methods:
$ python main.py -d exp
$ python main.py -d mix
$ python main.py -d copula
The other arguments are automatically set to reproduce the ALMOND.
One example that confirms the reliability of my implementation is that the results on mix data are similar to those presented in the original paper. My results failed to capture the density in the central region (see the figure). However, the lower right panel of Figure 1 in the original paper also showed similar results.