Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Reinforced Continual Learning   Most artificial intelligence models have limiting ability to solve new tasks
faster, without forgetting previously acquired knowledge. The recently emerging
paradigm of continual learning aims to solve this issue, in which the model
learns various tasks in a sequential fashion. In this work, a novel approach
for continual learning is proposed, which searches for the best neural
architecture for each coming task via sophisticatedly designed reinforcement
learning strategies. We name it as Reinforced Continual Learning. Our method
not only has good performance on preventing catastrophic forgetting but also
fits new tasks well. The experiments on sequential classification tasks for
variants of MNIST and CIFAR-100 datasets demonstrate that the proposed approach
outperforms existing continual learning alternatives for deep networks.

36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin