all projects tagged machine learning

Our approach to explainable AI, in particular generative models, is to apply methods from information geometry to latent spaces and find analogies in concepts of differential geometry applied to latent manifolds and general relativity.

Monte-Carlo Markov-chain methods are widely (and wildly) used in cosmological inference and can always be mapped back onto a canonical, statistical physics system. Interesting questions we pursue concern canonical parition functions reflecting cosmological, non-Gaussian likelihoods and analytical methods for inference for these cases.

Machine learning methods can help infering fundamental laws of Nature from complex data or to design inference processes that are otherwise difficult to manage. We are trying to apply inference on inflationary potentials with machine learning methods and hopefully establish links to information geometry.