Yan Wu

Artificial Intelligence · with Lillicrap · 2018–2019

A researcher in the Science corpus: 4 papers; trusts 69 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 75 citations into it; #19,226 of 35,345 by trust here, #12,761 by citations
• Reinforcement Learning — 49 citations into it
• Computer Vision and Pattern Recognition — 38 citations into it; #18,731 of 21,575 by trust here, #18,782 by citations
• Generative Models — 21 citations into it; #1,705 of 1,956 by trust here, #1,796 by citations
• Model Architectures — 21 citations into it
• Probabilistic & Bayesian Methods — 14 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Optimization & Training — 8 citations into it

Whom Yan Wu trusts overall

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

1. Diederik P. Kingma
2. Danilo Jimenez Rezende
3. Geoffrey E. Hinton
4. Pentti Kanerva
5. Yoshua Bengio
6. Alex Graves
7. Ruslan Salakhutdinov
8. Kaiming He
9. David John Cameron MacKay
10. John J. Hopfield

Who trusts Yan Wu overall

1. Karol Gregor
2. David R Ha
3. Danilo Jimenez Rezende
4. J. Urgen Schmidhuber
5. Jason Ramapuram
6. Michael L. Iuzzolino
7. Frederic Besse
8. Greg Wayne

Papers from Yan Wu's perspective

Ordered by how strongly the researchers Yan Wu 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. 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Shaping Belief States with Generative Environment Models for RL (2019) — 124 citations
• Optimizing agent behavior over long time scales by transporting value (2018) — 139 citations
• The Kanerva Machine: A Generative Distributed Memory (2018) — 41 citations
• Learning Attractor Dynamics for Generative Memory (2018) — 18 citations