  Gradient-based meta-learning (GBML) with deep neural nets (DNNs) has become a
popular approach for few-shot learning. However, due to the non-convexity of
DNNs and the bi-level optimization in GBML, the theoretical properties of GBML
with DNNs remain largely unknown. In this paper, we first aim to answer the
following question: Does GBML with DNNs have global convergence guarantees? We
provide a positive answer to this question by proving that GBML with
over-parameterized DNNs is guaranteed to converge to global optima at a linear
rate. The second question we aim to address is: How does GBML achieve fast
adaption to new tasks with prior experience on past tasks? To answer it, we
theoretically show that GBML is equivalent to a functional gradient descent
operation that explicitly propagates experience from the past tasks to new
ones, and then we prove a generalization error bound of GBML with
over-parameterized DNNs.
