Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Modular Meta-Learning with Shrinkage   Many real-world problems, including multi-speaker text-to-speech synthesis,
can greatly benefit from the ability to meta-learn large models with only a few
task-specific components. Updating only these task-specific modules then allows
the model to be adapted to low-data tasks for as many steps as necessary
without risking overfitting. Unfortunately, existing meta-learning methods
either do not scale to long adaptation or else rely on handcrafted
task-specific architectures. Here, we propose a meta-learning approach that
obviates the need for this often sub-optimal hand-selection. In particular, we
develop general techniques based on Bayesian shrinkage to automatically
discover and learn both task-specific and general reusable modules.
Empirically, we demonstrate that our method discovers a small set of meaningful
task-specific modules and outperforms existing meta-learning approaches in
domains like few-shot text-to-speech that have little task data and long
adaptation horizons. We also show that existing meta-learning methods including
MAML, iMAML, and Reptile emerge as special cases of our method.

17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
27
The categories of this paper are: comp-gas
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th