Phong Le

Artificial Intelligence · 2014–2019

A researcher in the Science corpus: 4 papers; trusts 164 researchers and is trusted by 39 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 55 citations into it; #4,425 of 35,345 by trust here, #11,921 by citations
• Natural Language Processing — 55 citations into it; #1,774 of 5,688 by trust here, #3,574 by citations
• Management Science and Operations Research — 13 citations into it; #4,593 of 11,946 by trust here, #6,024 by citations
• Representation & Self-Supervised Learning — 13 citations into it
• Model Architectures — 7 citations into it; #937 of 2,278 by trust here, #1,976 by citations
• Information Extraction — 6 citations into it; #268 of 407 by trust here, #361 by citations

Whom Phong Le trusts overall

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

1. Richard Socher
2. Tomas Mikolov
3. Diederik P. Kingma
4. Christopher D. Manning
5. Michael Collins
6. Yoshua Bengio
7. Geoffrey E. Hinton
8. John C. Duchi
9. Ronan Collobert
10. Yann LeCun

Who trusts Phong Le overall

1. Yoav Goldberg
2. Eliyahu Kiperwasser
3. Ivan Titov
4. Greg Durrett
5. Diego Marcheggiani
6. Yasumasa Onoe
7. Yanpeng Zhao
8. Jean Maillard
9. Nicola De Cao
10. Chris Dyer

Papers from Phong Le's perspective

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

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

Their papers

• The Inside-Outside Recursive Neural Network model for Dependency Parsing (2014) — 73 citations
• Improving Entity Linking by Modeling Latent Relations between Mentions (2018) — 227 citations
• The Forest Convolutional Network: Compositional Distributional Semantics with a Neural Chart and without Binarization (2015) — 37 citations
• Boosting Entity Linking Performance by Leveraging Unlabeled Documents (2019) — 45 citations