  In this article, we review the application of modern machine-learning (ML)
techniques to boost the search for processes involving the top quarks at the
LHC. We revisit the formalism of Convolutional Neural Networks (CNNs), Graph
Neural Networks (GNNs), and Attention Mechanisms. Based on recent studies, we
explore their applications in designing improved top taggers, top
reconstruction, and event classification tasks. We also examine the ML-based
likelihood-free inference approach and generative unfolding models, focusing on
their applications to scenarios involving top quarks.
