Rachel Sparks

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Rachel Sparks trusts overall

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

  1. Sergey Ioffe
  2. Jia Deng
  3. Christian Szegedy
  4. Tianqi Chen
  5. Marc Modat
  6. Bradley C. Lowekamp
  7. Fabian Isensee
  8. Alexander V. Buslaev
  9. Geoffrey E. Hinton
  10. Alex Krizhevsky

Papers from Rachel Sparks's perspective

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

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

Their papers