I. Bayesian

2014

A researcher in the Science corpus: 1 paper; trusts 0 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Whom I. Bayesian trusts overall

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

  1. Noah D. Goodman
  2. T. Griffiths
  3. Vikash K. Mansinghka
  4. Romain Thibaux
  5. Josh Tenenbaum
  6. David John Cameron MacKay

Papers from I. Bayesian's perspective

Ordered by how strongly the researchers I. Bayesian 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. Church: a language for generative models
  4. Gradient-based learning applied to document recognition
  5. Learning Multiple Layers of Features from Tiny Images
  6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  7. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
  8. ImageNet: A large-scale hierarchical image database
  9. Long Short-Term Memory
  10. ImageNet Large Scale Visual Recognition Challenge

Their papers