What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data Article Swipe

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Yang Hu , Adriane Chapman , Guihua Wen , Wendy Hall ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2106.06410

Supervised machine learning has several drawbacks that make it difficult to use in many situations. Drawbacks include: heavy reliance on massive training data, limited generalizability and poor expressiveness of high-level semantics. Low-shot Learning attempts to address these drawbacks. Low-shot learning allows the model to obtain good predictive power with very little or no training data, where structured knowledge plays a key role as a high-level semantic representation of human. This article will review the fundamental factors of low-shot learning technologies, with a focus on the operation of structured knowledge under different low-shot conditions. We also introduce other techniques relevant to low-shot learning. Finally, we point out the limitations of low-shot learning, the prospects and gaps of industrial applications, and future research directions.

Related Topics

Computer Science

Shot (Pellet)

Artificial Intelligence

Mechanical Engineering

Concepts

Computer science Generalizability theory Shot (pellet) Artificial intelligence One shot Machine learning Semantics (computer science) Key (lock) Representation (politics) Data science Engineering Computer security Organic chemistry Law Political science Mechanical engineering Politics Chemistry Mathematics Statistics Programming language

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2106.06410
PDF: https://arxiv.org/pdf/2106.06410
OA Status: green
Cited By: 3
References: 278
Related Works: 10
OpenAlex ID: https://openalex.org/W3168122895

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DOI: https://doi.org/10.48550/arxiv.2106.06410

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Title: What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data

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Type: preprint

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Language: en

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Publication year: 2021

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Publication date: 2021-06-11

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Authors: Yang Hu, Adriane Chapman, Guihua Wen, Wendy Hall

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Landing page: https://arxiv.org/abs/2106.06410

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PDF URL: https://arxiv.org/pdf/2106.06410

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OA URL: https://arxiv.org/pdf/2106.06410

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Concepts: Computer science, Generalizability theory, Shot (pellet), Artificial intelligence, One shot, Machine learning, Semantics (computer science), Key (lock), Representation (politics), Data science, Engineering, Computer security, Organic chemistry, Law, Political science, Mechanical engineering, Politics, Chemistry, Mathematics, Statistics, Programming language

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Cited by: 3

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Citations by year (recent): 2025: 1, 2024: 2

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References (count): 278

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Related works (count): 10

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