Catherine Wong

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 5 papers; trusts 156 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 112 citations into it; #33,817 of 35,345 by trust here, #13,163 by citations
• Natural Language Processing — 62 citations into it
• Computer Vision and Pattern Recognition — 48 citations into it
• Information Systems — 25 citations into it
• Reinforcement Learning — 23 citations into it
• Optimization & Training — 19 citations into it
• Representation & Self-Supervised Learning — 18 citations into it
• Model Architectures — 16 citations into it

Whom Catherine Wong trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Percy Liang
4. Alex Krizhevsky
5. Diederik P. Kingma
6. Quoc V. Le
7. Ilya Sutskever
8. Barret Zoph
9. Kaiming He
10. Radford M. Neal

Who trusts Catherine Wong overall

1. Xin He
2. Krzysztof Maziarz
3. Quanming Yao
4. Kevin Ellis
5. Efi Kokiopoulou
6. Chao Xue
7. Jiemin Fang
8. Neil Houlsby
9. Andrea Gesmundo
10. Maxwell Nye

Papers from Catherine Wong's perspective

Ordered by how strongly the researchers Catherine Wong 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. Long Short-Term Memory
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• DreamCoder: growing generalizable, interpretable knowledge with wake–sleep Bayesian program learning (2020) — 266 citations
• DreamCoder: bootstrapping inductive program synthesis with wake-sleep library learning (2021) — 278 citations
• Leveraging Language to Learn Program Abstractions and Search Heuristics (2021) — 61 citations
• Transfer Learning with Neural AutoML (2018) — 118 citations
• Transfer Automatic Machine Learning (2018) — 10 citations