  Methods for processing point cloud information have seen a great success in
collider physics applications. One recent breakthrough in machine learning is
the usage of Transformer networks to learn semantic relationships between
sequences in language processing. In this work, we apply a modified Transformer
network called Point Cloud Transformer as a method to incorporate the
advantages of the Transformer architecture to an unordered set of particles
resulting from collision events. To compare the performance with other
strategies, we study jet-tagging applications for highly-boosted particles.
