Machine Learning
Variational linear methods for approximating posteriors
Explanation & example will follow # import torch nn = torch.nn class LinearVariationalNormal(nn.Module): def __init__(self, in_features, out_features, bias=True, ...
read articleQuantum ML models for periodic, spherical, and 3-D rotational data
In the a previous post about quantum variational inference on polynomial models, I showed how you can infer expectation values & uncertainties of discrete random variables E\mathscr{E}E by...
read articleProbabilistic quantum predictions via variational methods
Uncertainty is a fundamental of every day life – and not just in that we don’t know who’s gonna win the next election, or whether it will rain, or how long until the next bus comes. I mean that down...
read articleBayesian inference in quantum chemistry
Small introduction In the field of computational quantum chemistry, scientists model molecules using foundational physical models like density functional theory. These methods are electronic, which...
read article