Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-step Estimation for Gradient-based Meta-learning   Gradient-based meta-learning approaches have been successful in few-shot
learning, transfer learning, and a wide range of other domains. Despite its
efficacy and simplicity, the burden of calculating the Hessian matrix with
large memory footprints is the critical challenge in large-scale applications.
To tackle this issue, we propose a simple yet straightforward method to reduce
the cost by reusing the same gradient in a window of inner steps. We describe
the dynamics of the multi-step estimation in the Lagrangian formalism and
discuss how to reduce evaluating second-order derivatives estimating the
dynamics. To validate our method, we experiment on meta-transfer learning and
few-shot learning tasks for multiple settings. The experiment on meta-transfer
emphasizes the applicability of training meta-networks, where other
approximations are limited. For few-shot learning, we evaluate time and memory
complexities compared with popular baselines. We show that our method
significantly reduces training time and memory usage, maintaining competitive
accuracies, or even outperforming in some cases.

37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
3
The categories of this paper are: physics
7
The categories of this paper are: hep-th