Tingwu Wang

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 4 papers; trusts 183 researchers and is trusted by 37 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 109 citations into it; #5,870 of 35,345 by trust here, #6,378 by citations
• Reinforcement Learning — 103 citations into it; #507 of 2,242 by trust here, #502 by citations
• Computer Vision and Pattern Recognition — 44 citations into it
• Control and Systems Engineering — 32 citations into it; #865 of 8,257 by trust here, #1,213 by citations
• Computational Theory and Mathematics — 19 citations into it
• Management Science and Operations Research — 13 citations into it
• Biomedical Engineering — 12 citations into it; #880 of 10,734 by trust here, #6,773 by citations
• Optimization & Training — 12 citations into it

Whom Tingwu Wang trusts overall

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

1. Sergey Levine
2. Richard S. Sutton
3. John Schulman
4. Pieter Abbeel
5. Geoffrey E. Hinton
6. E. Todorov
7. J. Urgen Schmidhuber
8. Diederik P. Kingma
9. Yoshua Bengio
10. Alex Krizhevsky

Who trusts Tingwu Wang overall

1. Michael Janner
2. Sergey Levine
3. Ignasi Clavera
4. Danijar Hafner
5. Brandon Amos
6. Ayush Jain
7. Zichuan Lin
8. Pieter Abbeel
9. Aleksandra Malysheva
10. Tianhe Yu

Papers from Tingwu Wang's perspective

Ordered by how strongly the researchers Tingwu Wang 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• VirtualHome: Simulating Household Activities Via Programs (2018) — 691 citations
• Benchmarking Model-Based Reinforcement Learning (2019) — 394 citations
• Exploring Model-based Planning with Policy Networks (2019) — 164 citations
• NerveNet: Learning Structured Policy with Graph Neural Networks (2018) — 327 citations