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
Bayesian Inference in Quantum Chemistry As an example, methanol, with six atoms, has 6 ×\times× 3 = 18 input variables, one for each Cartesian coordinate. Each xj∈R18x_j \in \mathbf{R}^{18}xj∈R18...
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