Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Stateless Neural Meta-Learning using Second-Order Gradients   Deep learning typically requires large data sets and much compute power for
each new problem that is learned. Meta-learning can be used to learn a good
prior that facilitates quick learning, thereby relaxing these requirements so
that new tasks can be learned quicker; two popular approaches are MAML and the
meta-learner LSTM. In this work, we compare the two and formally show that the
meta-learner LSTM subsumes MAML. Combining this insight with recent empirical
findings, we construct a new algorithm (dubbed TURTLE) which is simpler than
the meta-learner LSTM yet more expressive than MAML. TURTLE outperforms both
techniques at few-shot sine wave regression and image classification on
miniImageNet and CUB without any additional hyperparameter tuning, at a
computational cost that is comparable with second-order MAML. The key to
TURTLE's success lies in the use of second-order gradients, which also
significantly increases the performance of the meta-learner LSTM by 1-6%
accuracy.

7
The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
19
The categories of this paper are: eess
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom