John Bradshaw

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 154 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

Whom John Bradshaw trusts overall

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

  1. Geoffrey E. Hinton
  2. Ian J. Goodfellow
  3. Alex Krizhevsky
  4. Diederik P. Kingma
  5. Radford M. Neal
  6. Alex Graves
  7. Yann LeCun
  8. Yoshua Bengio
  9. Ruslan Salakhutdinov
  10. Ilya Sutskever

Who trusts John Bradshaw overall

  1. Greg Yang
  2. Akisato Kimura
  3. Idan Achituve
  4. Ethan Fetaya
  5. Jeremiah Zhe Liu
  6. Joost R. van Amersfoort
  7. Zoubin Ghahramani
  8. Florian Tramèr
  9. Saikiran Bulusu
  10. Aviv Navon

Papers from John Bradshaw's perspective

Ordered by how strongly the researchers John Bradshaw 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
  10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers