Weiyao Wang

Computer Vision and Pattern Recognition · with Carin · 2017–2018

A researcher in the Science corpus: 4 papers; trusts 241 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 101 citations into it; #10,503 of 21,575 by trust here, #7,176 by citations
• Generative Models — 99 citations into it; #1,402 of 1,956 by trust here, #1,021 by citations
• Artificial Intelligence — 60 citations into it; #16,528 of 35,345 by trust here, #10,207 by citations
• Probabilistic & Bayesian Methods — 17 citations into it; #1,391 of 1,525 by trust here, #991 by citations
• Statistical and Nonlinear Physics — 14 citations into it
• Optimization & Training — 13 citations into it
• Representation & Self-Supervised Learning — 7 citations into it

Whom Weiyao Wang trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Yann LeCun
6. Ilya Sutskever
7. Kaiming He
8. Individualized Treat
9. Jimmy Ba
10. Tim Salimans

Who trusts Weiyao Wang overall

1. Fei Ye
2. Chunyuan Li
3. Yunchen Pu
4. Adrian G. Bors
5. Yizhe Zhang
6. Shengjia Zhao

Papers from Weiyao Wang's perspective

Ordered by how strongly the researchers Weiyao 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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. Going deeper with convolutions
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Symmetric Variational Autoencoder and Connections to Adversarial Learning (2017) — 73 citations
• Triangle Generative Adversarial Networks (2017) — 142 citations
• Adversarial Symmetric Variational Autoencoder (2017) — 81 citations
• JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets (2018) — 44 citations