APOGEE Net: An expanded spectral model of both low mass and high mass
  stars
  We train a convolutional neural network, APOGEE Net, to predict
$T_\mathrm{eff}$, $\log g$, and, for some stars, [Fe/H], based on the APOGEE
spectra. This is the first pipeline adapted for these data that is capable of
estimating these parameters in a self-consistent manner not only for low mass
stars, (such as main sequence dwarfs, pre-main sequence stars, and red giants),
but also high mass stars with $T_\mathrm{eff}$ in excess of 50,000 K, including
hot dwarfs and blue supergiants. The catalog of ~650,000 stars presented in
this paper allows for a detailed investigation of the star forming history of
not just the Milky Way, but also of the Magellanic clouds, as different type of
objects tracing different parts of these galaxies can be more cleanly selected
through their distinct placement in $T_\mathrm{eff}$-$\log g$ parameter space
than in previous APOGEE catalogs produced through different pipelines.
