Pradyumna Narayana

Computer Vision and Pattern Recognition · 2022

A researcher in the Science corpus: 1 paper; trusts 10 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Pradyumna Narayana trusts

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

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

Papers from Pradyumna Narayana's perspective

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

  1. Microsoft COCO: Common Objects in Context
  2. Improved Techniques for Training GANs
  3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  4. ImageNet classification with deep convolutional neural networks
  5. Attention is All you Need (Neural Information Processing Systems)
  6. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
  7. Learning Transferable Visual Models From Natural Language Supervision (International Conference on Machine Learning)
  8. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (Journal of machine learning research)
  9. U-Net: Convolutional Networks for Biomedical Image Segmentation (International Conference on Medical Image Computing and Computer-Assisted Intervention)
  10. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)

Their papers