Etienne Becht

Representation & Self-Supervised Learning · 2014–2022

A researcher in the Science corpus: 8 papers; trusts 3 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

Whom Etienne Becht trusts overall

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

  1. Leland McInnes
  2. L. V. D. van der Maaten
  3. John Healy
  4. Yvan Saeys
  5. Matthew N. McCall
  6. K. Davis
  7. Geoffrey E. Hinton
  8. S. van Gassen
  9. Erin F. Simonds

Who trusts Etienne Becht overall

  1. Dmitry Kobak
  2. Tim Sainburg
  3. Leland McInnes
  4. Philipp Berens
  5. Zixuan Cang
  6. J. Böhm
  7. Brian L. Hie
  8. Julien Racle
  9. Gerry Tonkin-Hill
  10. George C. Linderman

Papers from Etienne Becht's perspective

Ordered by how strongly the researchers Etienne Becht 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. Gradient-based learning applied to document recognition
  4. Attention is All you Need (Neural Information Processing Systems)
  5. Learning Multiple Layers of Features from Tiny Images
  6. Long Short-Term Memory
  7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  8. Going deeper with convolutions
  9. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia
  10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers