Chong Huang

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 4 papers; trusts 44 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 104 citations into it; #32,032 of 35,345 by trust here, #22,463 by citations
• Computer Vision and Pattern Recognition — 32 citations into it
• Safety, Robustness & Interpretability — 32 citations into it
• Safety Research — 28 citations into it; #4,856 of 5,829 by trust here, #3,512 by citations
• Privacy & Security — 24 citations into it
• Sociology and Political Science — 23 citations into it
• Generative Models — 15 citations into it; #1,736 of 1,956 by trust here, #1,766 by citations
• Representation & Self-Supervised Learning — 15 citations into it

Whom Chong Huang trusts overall

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

1. Flávio du Pin Calmon
2. Cynthia Dwork
3. Martín Abadi
4. Tan Minh Nguyen
5. J. Urgen Schmidhuber
6. Alex Krizhevsky
7. Geoffrey E. Hinton
8. Sergey Ioffe
9. Changchang Liu
10. Latanya Sweeney

Who trusts Chong Huang overall

1. Aleksei Triastcyn
2. Mohammad Malekzadeh
3. Mario Díaz
4. John Martinsson
5. Boi Faltings
6. Richard G. Clegg
7. Hsiang Hsu
8. Edvin Listo Zec
9. Flávio du Pin Calmon
10. Simon Caton

Papers from Chong Huang's perspective

Ordered by how strongly the researchers Chong Huang 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Learning Generative Adversarial RePresentations (GAP) under Fairness and Censoring Constraints (2019) — 22 citations
• Censored and Fair Universal Representations using Generative Adversarial Models (2019) — 18 citations
• Generative Adversarial Privacy: A Data-Driven Approach to Information-Theoretic Privacy (2018) — 44 citations
• Context-Aware Generative Adversarial Privacy (2017) — 167 citations