Machine-learning models are typically based on extremely complex artificial neural networks. Because of their complexity, they’re often referred to as “black boxes”. Stefanie Jegelka, an associate professor in the Department of Electrical Engineering and Computer Science at MIT, is trying to unpack these black boxes.
Jegelka is particularly interested in optimizing machine-learning models when input data are in the form of graphs. Graph data pose specific challenges: For instance, information in the data consists of both information about individual nodes and edges, as well as the structure — what is connected to what. In addition, graphs have mathematical symmetries that need to be respected by the machine-learning model so that, for instance, the same graph always leads to the same prediction. Building such symmetries into a machine-learning model is usually not easy.
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