GameNetViz Final Report
نویسندگان
چکیده
In complex machine learning models, it is often time-consuming and difficult for practitioners to see the intermediate computational sets that lead to the model’s solution. We present GameNetViz as system for visualising the intermediate computation sets in a complex behavioural game theory model. It leverages off a particularly useful property of the domain: the model repeatedly transforms small matrices and small vectors; while the model itself is highly parameterized and complicated, the intermediate outputs are low dimensional and simple. This allows direct visual encoding that is effective at showing all intermediate transformations in a single overview view, with further details available via tool-tips and linked-highlighting.
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