Kaiming He

Chinese computer scientist whose architectures and training methods underpin much of modern deep learningdeep learning, n.: Machine learning done with neural networks built from many stacked layers: raw data such as pixels, sound or text goes in at one end, and each layer turns what it receives into something more abstract.. At Microsoft Research Asia he introduced the weight-initialization scheme for ReLUReLU, abbr.: The most common rule a neuron uses to produce its output: keep the number if it is positive, replace it with zero if it is negative. Simple, fast and surprisingly effective. networks now named after him (2015), in a paper reporting what its authors called the first classifier to surpass human-level accuracy on ImageNetImageNet, prop. n.: A huge collection of more than 14 million labeled photos, sorted into object categories, whose yearly recognition contest from 2010 to 2017 sparked the deep learning boom., and, with Xiangyu Zhang, Shaoqing Ren and Jian Sun, ResNetResNet, prop. n.: A family of image-recognition networks from 2015 whose shortcut connections allowed far deeper networks than before, over 100 layers, and became a standard building block across AI. (2015), whose residual connectionsresidual connection, n.: A shortcut in a neural network that adds a block's input straight to its output, so each block only has to learn a correction. It is what makes networks hundreds of layers deep trainable. made networks of hundreds of layers trainable; the paper is among the most cited of the century, and residual connections are part of every transformertransformer, n.: The design behind today's chatbots and much of modern AI. It handles text by letting every word look at every other word and decide which of them matter for understanding it.. At Facebook AI Research (2016–2024) he co-developed Mask R-CNN for instance segmentationinstance segmentation, n.: Finding each separate object in an image and tracing its exact outline, so that two people standing together get two distinct shapes rather than one blob., focal lossfocal loss, n.: A way of scoring a model's mistakes during training that pays less attention to examples it already gets right easily, so that learning concentrates on the hard and rare cases. for object detectionobject detection, n.: Finding every object of interest in a picture or video frame, saying what each one is and drawing a box around it, for example all the cars, cyclists and pedestrians in a street scene., the self-supervised momentum-contrast (MoCo) method and masked autoencodersmasked autoencoder, n.: A way to teach a vision model without labels: hide most of an image's tiles and train the model to fill in the missing parts, so that it learns what images are made of. (2021). He joined the faculty of the Massachusetts Institute of Technology in 2024 and also works part-time at Google DeepMindGoogle DeepMind, prop. n.: Google's main artificial intelligence laboratory, formed in 2023 by joining DeepMind and Google Brain. It builds the Gemini models and scientific AI systems such as AlphaFold. as a distinguished scientist. His papers have won best-paper awards at CVPR (2009, 2016) and ICCV (2017).