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Residual networks are easier to optimize than plain ones and keep gaining accuracy as depth increases.

Drawn from what Jian Sun and Kaiming He said

Subjectneural networks
What this subject means

neural networks The architecture almost all modern machine learning is built on, and what changes when it gets deeper.

Interesting
A private bookmark. Not a position, and never counted.

What they actually said

Word for word, with the source under each one. They did not write this page.

  1. Jian Sun We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. arxiv.org

    Computer vision researcher

    Their wordsWe provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.

    Where this was said

    ↗He, Zhang, Ren & Sun, "Deep Residual Learning for Image Recognition" (arXiv)arxiv.org

    All 8 korrents from this piecethis one is 6th

  2. Kaiming He We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. arxiv.org

    Associate professor at MIT and first author of the residual networks paper

    Their wordsWe provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.

    Where this was said

    ↗He, Zhang, Ren & Sun, "Deep Residual Learning for Image Recognition" (arXiv)arxiv.org

    All 8 korrents from this piecethis one is 5th

Added to korrents 10 Dec 2015 · How quotes work · Something wrong? Tell us

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