Sandy H. Huang

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 2 papers; trusts 20 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 46 citations into it; #34,562 of 35,345 by trust here, #8,810 by citations
• Reinforcement Learning — 33 citations into it
• Safety, Robustness & Interpretability — 12 citations into it; #2,161 of 2,251 by trust here, #799 by citations
• Control and Systems Engineering — 11 citations into it

Whom Sandy H. Huang trusts overall

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

1. Ian J. Goodfellow
2. Pieter Abbeel
3. Volodymyr Mnih
4. John Schulman
5. Christian Szegedy
6. Geoffrey E. Hinton
7. Sergey Levine
8. Richard S. Sutton
9. Yoshua Bengio
10. Diederik P. Kingma

Who trusts Sandy H. Huang overall

1. Yen-Chen Lin
2. Nicholas Carlini
3. Aditi Raghunathan
4. Kenneth T. Co
5. Katja Auernhammer
6. Ramitha Sundar
7. Stefan Webb
8. Anay Pattanaik
9. Huan Zhang
10. Ho Bae

Papers from Sandy H. Huang's perspective

Ordered by how strongly the researchers Sandy H. 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Adversarial Attacks on Neural Network Policies (2017) — 956 citations
• A Distributional View on Multi-Objective Policy Optimization (2020) — 94 citations