Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The effects of negative adaptation in Model-Agnostic Meta-Learning   The capacity of meta-learning algorithms to quickly adapt to a variety of
tasks, including ones they did not experience during meta-training, has been a
key factor in the recent success of these methods on few-shot learning
problems. This particular advantage of using meta-learning over standard
supervised or reinforcement learning is only well founded under the assumption
that the adaptation phase does improve the performance of our model on the task
of interest. However, in the classical framework of meta-learning, this
constraint is only mildly enforced, if not at all, and we only see an
improvement on average over a distribution of tasks. In this paper, we show
that the adaptation in an algorithm like MAML can significantly decrease the
performance of an agent in a meta-reinforcement learning setting, even on a
range of meta-training tasks.

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The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
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The categories of this paper are: math-ph
18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat