Lea Müller

Computer Vision and Pattern Recognition · 2021

A researcher in the Science corpus: 1 paper; trusts 7 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Lea Müller trusts overall

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

  1. Federica Bogo
  2. Diederik P. Kingma
  3. Michael J. Black
  4. Georgios Pavlakos
  5. Dong C. Liu
  6. Jimmy Ba
  7. Andrei Zanfir
  8. Donglai Xiang
  9. Nikos Kolotouros
  10. Geoffrey E. Hinton

Papers from Lea Müller's perspective

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

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

Their papers