  We present a new catalog of $9318$ Ly$\alpha$ emitter (LAE) candidates at $z
= 2.2$, $3.3$, $4.9$, $5.7$, $6.6$, and $7.0$ that are photometrically selected
by the SILVERRUSH program with a machine learning technique from large area (up
to $25.0$ deg$^2$) imaging data with six narrowband filters taken by the Subaru
Strategic Program with Hyper Suprime-Cam (HSC SSP) and a Subaru intensive
program, Cosmic HydrOgen Reionization Unveiled with Subaru (CHORUS). We
construct a convolutional neural network that distinguishes between real LAEs
and contaminants with a completeness of $94$% and a contamination rate of $1$%,
enabling us to efficiently remove contaminants from the photometrically
selected LAE candidates. We confirm that our LAE catalogs include $177$ LAEs
that have been spectroscopically identified in our SILVERRUSH programs and
previous studies, ensuring the validity of our machine learning selection. In
addition, we find that the object-matching rates between our LAE catalogs and
our previous results are $\simeq 80$-$100$% at bright NB magnitudes of
$\lesssim 24$ mag. We also confirm that the surface number densities of our LAE
candidates are consistent with previous results. Our LAE catalogs will be made
public on our project webpage.
