Nathaniel Wong

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 4 papers; trusts 59 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 117 citations into it; #30,535 of 35,345 by trust here, #11,624 by citations
• Reinforcement Learning — 67 citations into it; #2,035 of 2,242 by trust here, #1,253 by citations
• Computer Vision and Pattern Recognition — 50 citations into it; #18,777 of 21,575 by trust here, #10,677 by citations
• Natural Language Processing — 33 citations into it; #4,971 of 5,688 by trust here, #3,235 by citations
• Management Science and Operations Research — 13 citations into it
• Model Architectures — 12 citations into it
• Optimization & Training — 11 citations into it

Whom Nathaniel Wong trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Dzmitry Bahdanau
5. Alex Krizhevsky
6. Ilya Sutskever
7. Kaiming He
8. Yann LeCun
9. Alex Graves
10. Ronan Collobert

Who trusts Nathaniel Wong overall

1. Brenden M. Lake

Papers from Nathaniel Wong's perspective

Ordered by how strongly the researchers Nathaniel Wong 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
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

• Human Instruction-Following with Deep Reinforcement Learning via Transfer-Learning from Text (2020) — 92 citations
• Imitating Interactive Intelligence (2020) — 74 citations
• Open-Ended Learning Leads to Generally Capable Agents (2021) — 230 citations
• Robust Instruction-Following in a Situated Agent via Transfer-Learning from Text (2019)