  Deep learning is a rapidly-evolving technology with possibility to
significantly improve physics reach of collider experiments. In this study we
developed a novel algorithm of vertex finding for future lepton colliders such
as the International Linear Collider. We deploy two networks; one is simple
fully-connected layers to look for vertex seeds from track pairs, and the other
is a customized Recurrent Neural Network with an attention mechanism and an
encoder-decoder structure to associate tracks to the vertex seeds. The
performance of the vertex finder is compared with the standard ILC
reconstruction algorithm.
