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In the second part of his thesis, Ma shows how to understand and interpret the properties of embedding models for natural languages, which were learned using nonconvex optimization.
Ma is an Assistant Professor of Computer Science and Statistics at Stanford University.
However, much of the initial work in meta-learning focused on designing increasingly complex neural network architectures.
In her dissertation, Finn introduced a class of methods called model-agnostic meta-learning (MAML) methods, which don’t require computer scientists to manually design complex architectures.