  The All-Sky Automated Survey for Supernovae (ASAS-SN) is the first optical
survey to monitor the entire sky, currently with a cadence of $\lesssim 24$
hours down to $g \lesssim 18.5$ mag. ASAS-SN has routinely operated since 2013,
collecting $\sim$ 2,000 to over 7,500 epochs of $V$ and $g-$band observations
per field to date. This work illustrates the first analysis of ASAS-SN's newer,
deeper, higher cadence $g-$band data. From an input source list of ${\sim}55$
million isolated sources with $g<18$~mag, we identified $1.5\times10^6$
variable star candidates using a random forest classifier trained on features
derived from $\textit{Gaia}$, 2MASS, and AllWISE. Using ASAS-SN $g-$band light
curves, and an updated random forest classifier augmented with data from
Citizen ASAS-SN, we classified the candidate variables into 8 broad variability
types. We present a catalog of ${\sim}116,000$ new variable stars with high
classification probabilities, including ${\sim}111,000$ periodic variables and
${\sim}5,000$ irregular variables. We also recovered ${\sim}263,000$ known
variable stars.
