Shuyang Dai

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 5 papers; trusts 347 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 121 citations into it; #14,658 of 21,575 by trust here, #10,500 by citations
• Artificial Intelligence — 117 citations into it; #23,735 of 35,345 by trust here, #18,186 by citations
• Generative Models — 76 citations into it
• Probabilistic & Bayesian Methods — 41 citations into it
• Representation & Self-Supervised Learning — 35 citations into it
• Optimization & Training — 23 citations into it
• Statistical and Nonlinear Physics — 15 citations into it
• Management Science and Operations Research — 6 citations into it

Whom Shuyang Dai trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Kaiming He
5. Alex Krizhevsky
6. Yann LeCun
7. Ross B. Girshick
8. Ilya Sutskever
9. Radford M. Neal
10. Karen Simonyan

Who trusts Shuyang Dai overall

1. Fei Ye
2. Yibo Yang
3. Tian Han
4. Chunyuan Li

Papers from Shuyang Dai's perspective

Ordered by how strongly the researchers Shuyang Dai 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Symmetric Variational Autoencoder and Connections to Adversarial Learning (2017) — 73 citations
• CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information (2020) — 541 citations
• JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets (2018) — 44 citations
• Adaptation Across Extreme Variations using Unlabeled Domain Bridges (2019) — 13 citations
• On Fenchel Mini-Max Learning (2019) — 16 citations