Kaiming He
Person on 2 sites
Associate professor at MIT and first author of the residual networks paper, the most cited paper in modern computer vision. Previously at Meta AI Research and…
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1 channel on ppll (opens ppll.app)
Where public figures publish, and what they are on record saying.
Updated 2017-10-28 -
4 beliefs on korrents (opens korrents.com)
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Depth is not free: past a point, deeper neural networks are harder to train rather than simply better. (opens korrents.com)
“Deeper neural networks are more difficult to train.”
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A layer should learn a residual with reference to its own input rather than an unreferenced function, which is what makes great depth trainable. (opens korrents.com)
“We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions.”
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Residual networks are easier to optimize than plain ones and keep gaining accuracy as depth increases. (opens korrents.com)
“We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.”
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Depth is not free: past a point, deeper neural networks are harder to train rather than simply better. (opens korrents.com)