Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Global Convergence and Generalization Bound of Gradient-Based
  Meta-Learning with Deep Neural Nets   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.

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The categories of this paper are: q-alg
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The categories of this paper are: alg-geom
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The categories of this paper are: math-ph
2
The categories of this paper are: math
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The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
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The categories of this paper are: hep-ex
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The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
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The categories of this paper are: comp-gas
3
The categories of this paper are: physics
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The categories of this paper are: supr-con
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The categories of this paper are: patt-sol
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The categories of this paper are: atom-ph
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The categories of this paper are: quant-ph