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• W3127582016 abstract "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 Polya-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." @default.
• W3127582016 created "2021-02-15" @default.
• W3127582016 creator A5000111344 @default.
• W3127582016 creator A5061896635 @default.
• W3127582016 date "2021-05-03" @default.
• W3127582016 modified "2023-09-23" @default.
• W3127582016 title "Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes" @default.
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