Data-Driven Methods for Learning Sparse Graphical Models
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Nov 30, 2017 5:30pm to 7:00pm
1011 Evans Hall
Happening As Scheduled
Learning models from data has a significant impact on many disciplines, including computer vision, medical imaging, social networks, neuroscience and signal processing. In the network inference problem, one may model the relationships between the network components through an underlying inverse covariance matrix. Learning this graphical model is often challenged by the fact that only a small...
Somayeh Sojoudi, EECS, Mechanical Engineering (Speaker)