Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The Optimal Noise in Noise-Contrastive Learning Is Not What You Think   Learning a parametric model of a data distribution is a well-known
statistical problem that has seen renewed interest as it is brought to scale in
deep learning. Framing the problem as a self-supervised task, where data
samples are discriminated from noise samples, is at the core of
state-of-the-art methods, beginning with Noise-Contrastive Estimation (NCE).
Yet, such contrastive learning requires a good noise distribution, which is
hard to specify; domain-specific heuristics are therefore widely used. While a
comprehensive theory is missing, it is widely assumed that the optimal noise
should in practice be made equal to the data, both in distribution and
proportion. This setting underlies Generative Adversarial Networks (GANs) in
particular. Here, we empirically and theoretically challenge this assumption on
the optimal noise. We show that deviating from this assumption can actually
lead to better statistical estimators, in terms of asymptotic variance. In
particular, the optimal noise distribution is different from the data's and
even from a different family.

21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
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The categories of this paper are: mtrl-th
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The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
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The categories of this paper are: patt-sol