Richard Weber

Artificial Intelligence · 2007–2018

A researcher in the Science corpus: 5 papers; trusts 25 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

Whom Richard Weber trusts overall

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

  1. Yuhai Wu
  2. Alain Rakotomamonjy
  3. Isabelle M Guyon
  4. Jason Aaron Edward Weston
  5. Georg Dorffner
  6. Paul S. Bradley
  7. John Shawe-Taylor
  8. Andre´ Elisseeff
  9. Julian J. Faraway
  10. Gunnar Rätsch

Who trusts Richard Weber overall

  1. Jessie Y.C. Chen
  2. Sebastián Maldonado
  3. Haoyue Liu
  4. Min-Ling Zhang
  5. Robert Fildes
  6. Patrícia Ramos
  7. Chih-Feng Liu
  8. Verónica Bolón-Canedo
  9. Harsurinder Kaur
  10. Alan Jović

Papers from Richard Weber's perspective

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

  1. ImageNet classification with deep convolutional neural networks
  2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  3. Gradient-based learning applied to document recognition
  4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  5. ImageNet: A large-scale hierarchical image database
  6. Attention is All you Need (Neural Information Processing Systems)
  7. Long Short-Term Memory
  8. Learning Multiple Layers of Features from Tiny Images
  9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
  10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers