J. Barry

Optimization & Training · with Aerts · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 1 researcher and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom J. Barry trusts overall

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

  1. Lutz Prechelt
  2. Chintan Parmar
  3. Yoav Benjamini
  4. Geoffrey E. Hinton
  5. Robert M. Bell
  6. Trevor J. Hastie
  7. Alex Krizhevsky
  8. Nitish Srivastava
  9. Mina Rezaei
  10. Gavin Brown

Papers from J. Barry's perspective

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

  1. ImageNet classification with deep convolutional neural networks
  2. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
  3. Dropout: a simple way to prevent neural networks from overfitting
  4. Layer Normalization
  5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  6. Deep Learning
  7. Gradient-based learning applied to document recognition
  8. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  9. Long Short-Term Memory
  10. Learning Multiple Layers of Features from Tiny Images

Their papers