Kaiming He
Associate Professor at MIT EECS and Distinguished Scientist at Google DeepMind; first author of the Deep Residual Learning (ResNet) paper, among the most-cited papers of the century.
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Where they publish
Beliefs
Korrents What they believe 4 beliefs — each backed by an exact quote.
Each is a — compiled by korrents.com, not by them: the one-line wordings are korrents', the quotes are theirs.
Recent
Depth is not free: past a point, deeper neural networks are harder to train rather than simply better.
Deeper neural networks are more difficult to train.
He, Zhang, Ren & Sun, "Deep Residual Learning for Image Recognition" (arXiv) Said 10 Dec 2015
A layer should learn a residual with reference to its own input rather than an unreferenced function, which is what makes great depth trainable.
We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions.
He, Zhang, Ren & Sun, "Deep Residual Learning for Image Recognition" (arXiv) Said 10 Dec 2015
Residual networks are easier to optimize than plain ones and keep gaining accuracy as depth increases.
We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
He, Zhang, Ren & Sun, "Deep Residual Learning for Image Recognition" (arXiv) Said 10 Dec 2015
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The depth of a representation, not any particular architecture trick, is what most visual recognition tasks turn on.
The depth of representations is of central importance for many visual recognition tasks.
He, Zhang, Ren & Sun, "Deep Residual Learning for Image Recognition" (arXiv) Said 10 Dec 2015
Beliefs others hold too
Depth is not free: past a point, deeper neural networks are harder to train rather than simply better. 2 hold this
Deeper neural networks are more difficult to train.
He, Zhang, Ren & Sun, "Deep Residual Learning for Image Recognition" (arXiv) Said 10 Dec 2015
A layer should learn a residual with reference to its own input rather than an unreferenced function, which is what makes great depth trainable. 2 hold this
We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions.
He, Zhang, Ren & Sun, "Deep Residual Learning for Image Recognition" (arXiv) Said 10 Dec 2015
Residual networks are easier to optimize than plain ones and keep gaining accuracy as depth increases. 2 hold this
We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
He, Zhang, Ren & Sun, "Deep Residual Learning for Image Recognition" (arXiv) Said 10 Dec 2015
What is a korrent?
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