Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bayesian Few-Shot Classification with One-vs-Each P\'olya-Gamma
  Augmented Gaussian Processes   Few-shot classification (FSC), the task of adapting a classifier to unseen
classes given a small labeled dataset, is an important step on the path toward
human-like machine learning. Bayesian methods are well-suited to tackling the
fundamental issue of overfitting in the few-shot scenario because they allow
practitioners to specify prior beliefs and update those beliefs in light of
observed data. Contemporary approaches to Bayesian few-shot classification
maintain a posterior distribution over model parameters, which is slow and
requires storage that scales with model size. Instead, we propose a Gaussian
process classifier based on a novel combination of P\'olya-Gamma augmentation
and the one-vs-each softmax approximation that allows us to efficiently
marginalize over functions rather than model parameters. We demonstrate
improved accuracy and uncertainty quantification on both standard few-shot
classification benchmarks and few-shot domain transfer tasks.

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The categories of this paper are: math
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The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
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The categories of this paper are: q-alg
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The categories of this paper are: bayes-an
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The categories of this paper are: quant-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: eess
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The categories of this paper are: hep-th
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The categories of this paper are: acc-phys
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The categories of this paper are: q-fin
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The categories of this paper are: hep-lat
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The categories of this paper are: supr-con