Luke Gallagher

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 2 papers; trusts 15 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Luke Gallagher trusts overall

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

  1. Jerome H. Friedman
  2. C. Burges
  3. Tie-Yan Liu
  4. Zhuyun Dai
  5. Lidan Wang
  6. Olivier Chapelle
  7. James Bergstra
  8. Yoshua Bengio
  9. Jimmy J. Lin
  10. Chengxiang Zhai

Papers from Luke Gallagher's perspective

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

  1. ImageNet classification with deep convolutional neural networks
  2. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
  3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  5. Long Short-Term Memory
  6. Attention is All you Need (Neural Information Processing Systems)
  7. Gradient-based learning applied to document recognition
  8. ImageNet: A large-scale hierarchical image database
  9. Learning Multiple Layers of Features from Tiny Images
  10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers