Ashley Hill

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 3 papers; trusts 14 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

Whom Ashley Hill trusts overall

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

  1. Rico Jonschkowski
  2. Diederik P. Kingma
  3. Geoffrey E. Hinton
  4. Volodymyr Mnih
  5. Kaiming He
  6. J. Urgen Schmidhuber
  7. Yoshua Bengio
  8. Alex Krizhevsky
  9. Richard S. Sutton
  10. Yann LeCun

Who trusts Ashley Hill overall

  1. Hugo Caselles-Dupré
  2. Alexandre Heuillet
  3. Michaël Garcia Ortiz
  4. Kalifou René Traoré
  5. Timothée Lesort
  6. David Filliat
  7. Fabien Couthouis
  8. Natalia Díaz Rodríguez

Papers from Ashley Hill's perspective

Ordered by how strongly the researchers Ashley Hill 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. Gradient-based learning applied to document recognition
  5. Long Short-Term Memory
  6. Attention is All you Need (Neural Information Processing Systems)
  7. ImageNet: A large-scale hierarchical image database
  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