Vicki Cheung

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 3 papers; trusts 6 researchers and is trusted by 2,924 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 57 citations into it
• Computer Vision and Pattern Recognition — 25 citations into it; #714 of 21,575 by trust here, #586 by citations
• Optimization & Training — 11 citations into it; #217 of 2,601 by trust here, #82 by citations
• Generative Models — 10 citations into it; #106 of 1,956 by trust here, #55 by citations
• Evaluation & Benchmarks — 7 citations into it; #779 of 3,147 by trust here, #335 by citations
• Signal Processing — 6 citations into it

Whom Vicki Cheung trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Ian J. Goodfellow
4. Ilya Sutskever
5. Christian Szegedy
6. Richard S. Sutton
7. Yoshua Bengio
8. Alex Krizhevsky
9. Ronald J. Williams
10. Yann LeCun

Who trusts Vicki Cheung overall

1. Sergey Levine
2. Chunyuan Li
3. Pieter Abbeel
4. Yoshua Bengio
5. David R Ha
6. Chelsea Finn
7. Trevor Darrell
8. Yilun Du
9. Zhe Gan
10. Dacheng Tao

Papers from Vicki Cheung's perspective

Ordered by how strongly the researchers Vicki Cheung 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. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. GENERATIVE ADVERSARIAL NETS
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Improved Techniques for Training GANs (2016) — 10,497 citations
• Dota 2 with Large Scale Deep Reinforcement Learning (2019) — 2,179 citations
• OpenAI Gym (2016) — 5,596 citations