Nathan Ng

Artificial Intelligence · with Auli · 2017–2020

A researcher in the Science corpus: 5 papers; trusts 215 researchers and is trusted by 151 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 112 citations into it; #1,903 of 35,345 by trust here, #1,960 by citations
• Natural Language Processing — 99 citations into it; #762 of 5,688 by trust here, #489 by citations
• Computer Vision and Pattern Recognition — 64 citations into it; #4,057 of 21,575 by trust here, #1,702 by citations
• Model Architectures — 17 citations into it
• Evaluation & Benchmarks — 16 citations into it
• Representation & Self-Supervised Learning — 15 citations into it; #2,607 of 3,145 by trust here, #3,009 by citations
• Large Language Models — 9 citations into it
• Optimization & Training — 8 citations into it

Whom Nathan Ng trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Ilya Sutskever
6. Yann LeCun
7. Philipp Koehn
8. Myle Ott
9. Karen Simonyan
10. Sepp Hochreiter

Who trusts Nathan Ng overall

1. Changhan Wang
2. Lei Yu
3. Peng-Jen Chen
4. Angela Fan
5. Zhengbao Jiang
6. Bryan Eikema
7. Graham Neubig
8. Anne Wu
9. Alexei Baevski
10. Dhruv Madeka

Papers from Nathan Ng's perspective

Ordered by how strongly the researchers Nathan Ng 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• fairseq: A Fast, Extensible Toolkit for Sequence Modeling (2019) — 3,399 citations
• Facebook FAIR’s WMT19 News Translation Task Submission (2019) — 424 citations
• Simple and Effective Noisy Channel Modeling for Neural Machine Translation (2019) — 83 citations
• SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness (2020) — 155 citations
• Predicting Surgery Duration with Neural Heteroscedastic Regression (2017) — 29 citations