  We present a new inference framework for neutron star astrophysics based on
conditional variational autoencoders. Once trained, the generator block of the
model reconstructs the neutron star equation of state from a given set of
mass-radius observations. While the pressure of dense matter is the focus of
the present study, the proposed model is flexible enough to accommodate the
reconstructing of any other quantity related to dense matter equation of state.
Our results show robust reconstructing performance of the model, allowing to
make instantaneous inference from any given observation set. The present
framework contrasts with computationally expensive evaluation of the
equation-of-state posterior probability distribution based on Markov chain
Monte Carlo methods.
