14 citations · 16 across the 3 of their papers we have counts for
3 papers
Improved Structural Discovery and Representation Learning of Multi-Agent Data
Jennifer Hobbs, Matthew Holbrook, Nathan Frank +2
Central to all machine learning algorithms is data representation. For multi-agent systems, selecting a representation which adequately captures the interactions among agents is ch…
Rugby-Bot: Utilizing Multi-Task Learning & Fine-Grained Features for Rugby League Analysis
Matthew Holbrook, Jennifer Hobbs, Patrick Lucey
Sporting events are extremely complex and require a multitude of metrics to accurate describe the event. When making multiple predictions, one should make them from a single source…
Fine-Grained Retrieval of Sports Plays using Tree-Based Alignment of Trajectories
Long Sha, Patrick Lucey, Stephan Zheng +3
We propose a novel method for effective retrieval of multi-agent spatiotemporal tracking data. Retrieval of spatiotemporal tracking data offers several unique challenges compared t…