Fengyi Tang

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 30 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 71 citations into it
• Natural Language Processing — 23 citations into it
• Computer Vision and Pattern Recognition — 22 citations into it
• Safety, Robustness & Interpretability — 20 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Model Architectures — 9 citations into it

Whom Fengyi Tang trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Ilya Sutskever
5. Jimmy Ba
6. Christian Szegedy
7. Alex Krizhevsky
8. Sepp Hochreiter
9. Tomas Mikolov
10. Yann LeCun

Who trusts Fengyi Tang overall

1. Fei Wang
2. Chao Yu
3. Anita M. Preininger
4. Jiming Liu
5. W. Zhang

Papers from Fengyi Tang's perspective

Ordered by how strongly the researchers Fengyi Tang 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. Learning Multiple Layers of Features from Tiny Images
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records (2019) — 121 citations
• Identify Susceptible Locations in Medical Records via Adversarial Attacks on Deep Predictive Models (2018) — 67 citations
• Improving Mild Cognitive Impairment Prediction via Reinforcement Learning and Dialogue Simulation (2018) — 9 citations