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Creators/Authors contains: "Chazelle, Bernard"

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  1. ; (, 10th International Conference on Complex Networks and their Applications)
    n this paper, we demonstrate the utility of dynamic network sequences to provide insight into geometric data; moreover, we construct a nat- ural syntactic and semantic understanding of these network sequences for useful downstream applications. As a proof-of-concept, we study the trajectory data of basketball players and construct “interaction networks” to express an essential game mechanic: the ability for the offensive team to pass the ball to each other. These networks give rise to a library of player configurations that can in turn be modeled by a jump Markov model. This model provides a highly compressed representation of a game, while capturing important latent structures. By lever- aging this structure, we use a Transformer to predict trajectories with increased accuracy. 
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    Accepted Manuscript Full Text Available