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Training Complex Models with Multi-Task Weak Supervision
As machine learning models continue to increase in complexity, collecting large hand-labeled training sets has become one of the biggest roadblocks in practice. Instead, weaker forms of supervision that provide noisier but cheaper labels are often used. However, these weak supervision sources have d...
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| 出版年: | Proc AAAI Conf Artif Intell |
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| 主要な著者: | , , , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
2019
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6765366/ https://ncbi.nlm.nih.gov/pubmed/31565535 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1609/aaai.v33i01.33014763 |
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