2 citations · 4 across the 2 of their papers we have counts for
5 papers
Exact and Approximate Hierarchical Clustering Using A*
Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath +6
Hierarchical clustering is a critical task in numerous domains. Many approaches are based on heuristics and the properties of the resulting clusterings are studied post hoc. Howeve…
Hierarchical clustering in particle physics through reinforcement learning
Johann Brehmer, Sebastian Macaluso, Duccio Pappadopulo +1
Particle physics experiments often require the reconstruction of decay patterns through a hierarchical clustering of the observed final-state particles. We show that this task can…
Data Structures & Algorithms for Exact Inference in Hierarchical Clustering
Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath +5
Hierarchical clustering is a fundamental task often used to discover meaningful structures in data, such as phylogenetic trees, taxonomies of concepts, subtypes of cancer, and casc…
The Machine Learning Landscape of Top Taggers
G. Kasieczka, T. Plehn, A. Butter +24
Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches. Unlike most established metho…
Pulling Out All the Tops with Computer Vision and Deep Learning
Sebastian Macaluso, David Shih
We apply computer vision with deep learning -- in the form of a convolutional neural network (CNN) -- to build a highly effective boosted top tagger. Previous work (the "DeepTop" t…