Mark Weber

Computer Vision and Pattern Recognition · with Cremers · 2022–2024

A researcher in the Science corpus: 2 papers; trusts 37 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Mark Weber trusts overall

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

  1. Kaiming He
  2. Geoffrey E. Hinton
  3. Sergey Ioffe
  4. Alex Krizhevsky
  5. Diederik P. Kingma
  6. Christian Szegedy
  7. Ross B. Girshick
  8. Yoshua Bengio
  9. Yann LeCun
  10. Ilya Sutskever

Papers from Mark Weber's perspective

Ordered by how strongly the researchers Mark Weber 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. Attention is All you Need (Neural Information Processing Systems)
  4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  5. ImageNet: A large-scale hierarchical image database
  6. Gradient-based learning applied to document recognition
  7. Long Short-Term Memory
  8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
  9. ImageNet Large Scale Visual Recognition Challenge
  10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers