Dr. Kaiming He
MIT EECS
Verified Proof of Work Artifacts
2 items catalogedEach artifact below represents an authenticated research publication, production code repository, or technical architectural framework directly authored or co-created by Dr. Kaiming He. Every entry undergoes editorial source verification.
Masked Autoencoders Are Scalable Vision Learners (MAE)
Developed Masked Autoencoders (MAE), proving that masking 75% of image patches and using an asymmetric vision transformer autoencoder allows models to learn rich visual representations self-supervised at massive scale.
Requires substantial pre-training compute and large unlabeled image datasets for optimal self-supervised convergence.
Deep Residual Learning for Image Recognition (ResNet)
First author of the CVPR 2016 Best Paper introducing identity skip connections (Residual Networks), overcoming vanishing gradients to train 100+ layer networks, which became standard across all modern deep learning and transformers.
Pure convolutional ResNets possess localized receptive fields, necessitating hybrid attention blocks for global context modeling.