Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph
  Embedding   Knowledge Graph (KG) embedding is a fundamental problem in data mining
research with many real-world applications. It aims to encode the entities and
relations in the graph into low dimensional vector space, which can be used for
subsequent algorithms. Negative sampling, which samples negative triplets from
non-observed ones in the training data, is an important step in KG embedding.
Recently, generative adversarial network (GAN), has been introduced in negative
sampling. By sampling negative triplets with large scores, these methods avoid
the problem of vanishing gradient and thus obtain better performance. However,
using GAN makes the original model more complex and hard to train, where
reinforcement learning must be used. In this paper, motivated by the
observation that negative triplets with large scores are important but rare, we
propose to directly keep track of them with the cache. However, how to sample
from and update the cache are two important questions. We carefully design the
solutions, which are not only efficient but also achieve a good balance between
exploration and exploitation. In this way, our method acts as a "distilled"
version of previous GA-based methods, which does not waste training time on
additional parameters to fit the full distribution of negative triplets. The
extensive experiments show that our method can gain significant improvement in
various KG embedding models, and outperform the state-of-the-art negative
sampling methods based on GAN.

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The categories of this paper are: astro-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-th
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The categories of this paper are: supr-con
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The categories of this paper are: econ
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The categories of this paper are: alg-geom
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The categories of this paper are: math
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The categories of this paper are: nucl-ex
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The categories of this paper are: ao-sci
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The categories of this paper are: quant-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: cmp-lg
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The categories of this paper are: mtrl-th