G. G. Lee

Artificial Intelligence · 2006–2018

A researcher in the Science corpus: 4 papers; trusts 41 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 32 citations into it; #16,252 of 35,345 by trust here, #19,873 by citations
• Natural Language Processing — 16 citations into it
• Representation & Self-Supervised Learning — 8 citations into it
• Optimization & Training — 7 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it
• Model Architectures — 6 citations into it

Whom G. G. Lee trusts overall

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

1. Thorsten Joachims
2. Hal Daumé Iii
3. Michael Collins
4. Vladimir Naumovich Vapnik
5. Trevor Cohn
6. Andrew McCallum
7. Diederik P. Kingma
8. Koby Crammer
9. Geoffrey E. Hinton
10. Fernando C Pereira

Who trusts G. G. Lee overall

1. Gökhan Tür
2. Y. Qian

Papers from G. G. Lee's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Long Short-Term Memory
3. ImageNet classification with deep convolutional neural networks
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. GENERATIVE ADVERSARIAL NETS
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. ImageNet: A large-scale hierarchical image database
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Multi-domain spoken language understanding with transfer learning (2009) — 57 citations
• Out-of-domain Detection based on Generative Adversarial Network (2018) — 70 citations
• Neural sentence embedding using only in-domain sentences for out-of-domain sentence detection in dialog systems (2017) — 56 citations
• Information gain and divergence-based feature selection for machine learning-based text categorization (2006) — 386 citations