  Data-driven models, which apply machine learning to infer physical properties
from large quantities of data, have become increasingly important for
extracting stellar properties from spectra. In general, these methods have been
applied to data in one wavelength regime or another. For example, APOGEE Net
has been applied to near-IR spectra from the SDSS-V APOGEE survey to predict
stellar parameters (Teff, log g, and [Fe/H]) for all stars with Teff from 3,000
to 50,000 K, including pre-main sequence stars, OB stars, main sequence dwarfs,
and red giants. The increasing number of large surveys across multiple
wavelength regimes provides the opportunity to improve data-driven models
through learning from multiple datasets at once. In SDSS-V, a number of spectra
of stars will be observed not just with APOGEE in near-IR, but also with BOSS
in optical regime. Here we aim to develop a complementary model, BOSS Net, that
will replicate the performance of APOGEE Net in these optical data through
label transfer. We further improve the model by extending it to brown dwarfs,
as well as white dwarfs, resulting in a comprehensive coverage between
1700<Teff<100,000 K and 0<log g<10, to ensure BOSS Net can reliably measure
parameters of most of the commonly observed objects within this parameter
space. We also update APOGEE Net to achieve a comparable performance in the
near-IR regime. The resulting models provide a robust tool for measuring
stellar evolutionary states, and in turn, enable characterization of the star
forming history of the Galaxy.
