Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Few-Shot Learning Through an Information Retrieval Lens   Few-shot learning refers to understanding new concepts from only a few
examples. We propose an information retrieval-inspired approach for this
problem that is motivated by the increased importance of maximally leveraging
all the available information in this low-data regime. We define a training
objective that aims to extract as much information as possible from each
training batch by effectively optimizing over all relative orderings of the
batch points simultaneously. In particular, we view each batch point as a
`query' that ranks the remaining ones based on its predicted relevance to them
and we define a model within the framework of structured prediction to optimize
mean Average Precision over these rankings. Our method achieves impressive
results on the standard few-shot classification benchmarks while is also
capable of few-shot retrieval.

22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th