Clayton Mullis

Computer Vision and Pattern Recognition · 2021–2022

A researcher in the Science corpus: 2 papers; trusts 11 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

Whom Clayton Mullis trusts

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Alex Krizhevsky (in Computer Vision and Pattern Recognition)
  2. Kaiming He (in Computer Vision and Pattern Recognition)
  3. Ilya Sutskever (in Computer Vision and Pattern Recognition)
  4. Li Fei-Fei (in Computer Vision and Pattern Recognition)
  5. Ross B. Girshick (in Computer Vision and Pattern Recognition)
  6. Andrew Zisserman (in Computer Vision and Pattern Recognition)
  7. Shaoqing Ren (in Computer Vision and Pattern Recognition)
  8. Jitendra Malik (in Computer Vision and Pattern Recognition)
  9. Geoffrey E. Hinton (in Computer Vision and Pattern Recognition)
  10. Karen Simonyan (in Computer Vision and Pattern Recognition)

Who trusts Clayton Mullis

  1. Srivatsa Kundurthy (in Computer Vision and Pattern Recognition)

Papers from Clayton Mullis's perspective

Ordered by how strongly the researchers Clayton Mullis trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Attention is All you Need (Neural Information Processing Systems)
  2. ImageNet: A large-scale hierarchical image database
  3. ImageNet Large Scale Visual Recognition Challenge
  4. Microsoft COCO: Common Objects in Context
  5. Language Models are Few-Shot Learners (Neural Information Processing Systems)
  6. ImageNet classification with deep convolutional neural networks
  7. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (International Conference on Learning Representations)
  8. Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
  9. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  10. Learning Transferable Visual Models From Natural Language Supervision (International Conference on Machine Learning)

Their papers