Manuel Kaufmann

Computer Vision and Pattern Recognition · 2020–2022

A researcher in the Science corpus: 2 papers; trusts 81 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Manuel Kaufmann trusts overall

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

  1. Geoffrey E. Hinton
  2. Diederik P. Kingma
  3. Daniel Holden
  4. Alex Krizhevsky
  5. Ilya Sutskever
  6. Yoshua Bengio
  7. Kaiming He
  8. Michael J. Black
  9. Nitish Srivastava
  10. Yann LeCun

Papers from Manuel Kaufmann's perspective

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

  1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  3. Attention is All you Need (Neural Information Processing Systems)
  4. ImageNet classification with deep convolutional neural networks
  5. ImageNet: A large-scale hierarchical image database
  6. Long Short-Term Memory
  7. Gradient-based learning applied to document recognition
  8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
  9. Dropout: a simple way to prevent neural networks from overfitting
  10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers