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papersSEP 10 04:00 UTC

Teacher Geometry Shapes Learnability in Teacher-Student Networks

An arXiv preprint studies teacher-student frameworks, where one neural network produces training data for another network that must learn to reproduce its behavior, a standard abstraction in learning theory. The authors find that the geometric structure of the teacher network plays a decisive role in determining whether and how well the student can learn the target function. The paper was posted as a new submission and cross-listed in the cs.AI and cs.LG categories.

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