Kaiming He
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 Microsoft Research Asia.
Kaiming He did not write this page.
We collected these quotes from things they published elsewhere, and every quote links to where it was said. They have no account here and have not endorsed this site. Quotes are word for word; the short line under each one is our own restatement, not their wording. Their own site. Is this you? Claim it or ask us to remove it. Or tell us what is wrong here.
Filter All topics · Newest first
-
Their wordsDeeper neural networks are more difficult to train.
↗He, Zhang, Ren & Sun, "Deep Residual Learning for Image Recognition" (arXiv)arxiv.org All 8 from this paper
-
Their wordsWe 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)arxiv.org All 8 from this paper
-
Their wordsWe 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)arxiv.org All 8 from this paper
-
neural networkscomputer vision
Their wordsThe depth of representations is of central importance for many visual recognition tasks.
↗He, Zhang, Ren & Sun, "Deep Residual Learning for Image Recognition" (arXiv)arxiv.org All 8 from this paper