Simple Mechanisms for Representing, Indexing and Manipulating Concepts
Yuanzhi Li Raghu Meka Rina Panigrahy Kulin Shah
Deep networks typically learn concepts via classifiers, which involves
setting up a model and training it via gradient descent to fit the
concept-labeled data. We will argue instead that learning a concept could be
done by looking at its moment statistics matrix to generate a concrete
representation or signature of that concept. These signatures can be used to
discover structure across the set of concepts and could recursively produce
higher-level concepts by learning this structure from those signatures. When
the concepts are `intersected', signatures of the concepts can be used to find
a common theme across a number of related `intersected' concepts. This process
could be used to keep a dictionary of concepts so that inputs could correctly
identify and be routed to the set of concepts involved in the (latent)
generation of the input.