Michael Houston

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 2 papers; trusts 15 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

Whom Michael Houston trusts

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

  1. Kaiming He (in Computer Vision and Pattern Recognition)
  2. Shaoqing Ren (in Computer Vision and Pattern Recognition)
  3. Andrew Zisserman (in Computer Vision and Pattern Recognition)
  4. X. Zhang (in Computer Vision and Pattern Recognition)
  5. Ross B. Girshick (in Computer Vision and Pattern Recognition)
  6. Alex Krizhevsky (in Computer Vision and Pattern Recognition)
  7. Karen Simonyan (in Computer Vision and Pattern Recognition)
  8. Geoffrey E. Hinton (in Computer Vision and Pattern Recognition)
  9. Ilya Sutskever (in Computer Vision and Pattern Recognition)
  10. Christian Szegedy (in Artificial Intelligence)

Who trusts Michael Houston

  1. Luke S. Zettlemoyer (in Computer Vision and Pattern Recognition)
  2. Boris Ginsburg (in Computer Vision and Pattern Recognition)
  3. Bryan Catanzaro (in Computer Vision and Pattern Recognition)

Papers from Michael Houston's perspective

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

  1. ImageNet classification with deep convolutional neural networks
  2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  3. Long Short-Term Memory
  4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  5. Attention is All you Need (Neural Information Processing Systems)
  6. Going deeper with convolutions
  7. ImageNet Large Scale Visual Recognition Challenge
  8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
  9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
  10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers