Shuangfei Zhai

Artificial Intelligence · 2015–2023

A researcher in the Science corpus: 10 papers; trusts 681 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 208 citations into it; #11,979 of 35,345 by trust here, #7,062 by citations
• Computer Vision and Pattern Recognition — 160 citations into it; #12,166 of 21,575 by trust here, #8,502 by citations
• Model Architectures — 72 citations into it
• Representation & Self-Supervised Learning — 42 citations into it; #1,976 of 3,145 by trust here, #1,370 by citations
• Optimization & Training — 41 citations into it
• Generative Models — 38 citations into it
• Reinforcement Learning — 34 citations into it
• Probabilistic & Bayesian Methods — 27 citations into it

Whom Shuangfei Zhai trusts overall

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

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

Who trusts Shuangfei Zhai overall

1. Yu Cheng
2. Chuhan Wu
3. Houssam Zenati
4. Mingxiao An
5. Yunfu Song
6. Izhak Golan
7. Fangzhao Wu
8. Duo Wang
9. Hyunjong Park
10. David W. Zhang

Papers from Shuangfei Zhai's perspective

Ordered by how strongly the researchers Shuangfei Zhai 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Fully-Adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification (2016) — 407 citations
• Deep Structured Energy Based Models for Anomaly Detection (2016) — 463 citations
• An Attention Free Transformer (2021) — 186 citations
• Doubly Convolutional Neural Networks (2016) — 66 citations
• Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning (2021) — 230 citations
• Stabilizing Transformer Training by Preventing Attention Entropy Collapse (2023) — 184 citations
• Dropout Training of Matrix Factorization and Autoencoder for Link Prediction in Sparse Graphs (2015) — 38 citations
• Adversarial Fisher Vectors for Unsupervised Representation Learning (2019) — 9 citations
• Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records (2017) — 180 citations
• DeepIntent: Learning Attentions for Online Advertising with Recurrent Neural Networks (2016) — 120 citations