  In this work, we present our classification algorithm to identify strong
gravitational lenses from wide-area surveys using machine learning
convolutional neural network; LensExtractor. We train and test the algorithm
using a wide variety of strong gravitational lens configurations from
simulations of lensing events. Images are processed through multiple
convolutional layers which extract feature maps necessary to assign a lens
probability to each image. LensExtractor provides a ranking scheme for all
sources which could be used to identify potential gravitational lens candidates
significantly reducing the number of images that have to be visually inspected.
We further apply our algorithm to the \textit{HST}/ACS i-band observations of
the COSMOS field and present our sample of identified lensing candidates. The
developed machine learning algorithm is much more computationally efficient
than classical lens identification algorithms and is ideal for discovering such
events across wide areas from current and future surveys such as LSST and
WFIRST.
