Neil R. Bramley

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 2 papers; trusts 14 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Neil R. Bramley trusts overall

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

  1. Patricia W. Cheng
  2. Radford M. Neal
  3. Eric R. Ziegel
  4. Stergios B. Fotopoulos
  5. Stuart Geman
  6. T. Griffiths
  7. Steffen L. Lauritzen
  8. H. J. Einhorn
  9. Winter A. Mason
  10. David J. Hauser

Papers from Neil R. Bramley's perspective

Ordered by how strongly the researchers Neil R. Bramley 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. Pattern Recognition and Machine Learning
  4. Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
  5. Gradient-based learning applied to document recognition
  6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  7. Long Short-Term Memory
  8. Attention is All you Need (Neural Information Processing Systems)
  9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
  10. Learning Multiple Layers of Features from Tiny Images

Their papers