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art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos
Open Access logo, converted into svg, designed by PLoS. This version with transparent background.
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art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos
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art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos
Open Access logo, converted into svg, designed by PLoS. This version with transparent background.
http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg
art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos
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art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos
Open Access logo, converted into svg, designed by PLoS. This version with transparent background.
http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg
art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos
http://www.plos.org/
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art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos
Open Access logo, converted into svg, designed by PLoS. This version with transparent background.
http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg
art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos
http://www.plos.org/
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image/svg+xml
art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos
Open Access logo, converted into svg, designed by PLoS. This version with transparent background.
http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg
art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos
http://www.plos.org/
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pytorch-meta software on GitHub
pytorch-meta software on GitHub in support of 'The Curse of Low Task Diversity: On the Failure of Transfer Learning to Outperform MAML and Their Empirical Equivalence'
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Related research
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Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes
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The Curse of Low Task Diversity: On the Failure of Transfer Learning to Outperform MAML and Their Empirical Equivalence
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Is Pre-training Truly Better Than Meta-Learning?
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On the Limits of Multi-modal Meta-Learning with Auxiliary Task Modulation Using Conditional Batch Normalization
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