Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Information-theoretical label embeddings for large-scale image
  classification   We present a method for training multi-label, massively multi-class image
classification models, that is faster and more accurate than supervision via a
sigmoid cross-entropy loss (logistic regression). Our method consists in
embedding high-dimensional sparse labels onto a lower-dimensional dense sphere
of unit-normed vectors, and treating the classification problem as a cosine
proximity regression problem on this sphere. We test our method on a dataset of
300 million high-resolution images with 17,000 labels, where it yields
considerably faster convergence, as well as a 7% higher mean average precision
compared to logistic regression.

22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph