Minhao Cheng

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 4 papers; trusts 149 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 122 citations into it; #13,805 of 35,345 by trust here, #9,275 by citations
• Safety, Robustness & Interpretability — 91 citations into it; #1,149 of 2,251 by trust here, #939 by citations
• Computer Vision and Pattern Recognition — 43 citations into it
• Natural Language Processing — 31 citations into it; #5,682 of 5,688 by trust here, #3,529 by citations
• Signal Processing — 29 citations into it; #4,412 of 7,572 by trust here, #2,990 by citations
• Model Architectures — 17 citations into it; #1,477 of 2,278 by trust here, #1,433 by citations
• Hardware and Architecture — 11 citations into it

Whom Minhao Cheng trusts overall

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

1. Alex Krizhevsky
2. Ilya Sutskever
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Yann LeCun
6. Kaiming He
7. Ian J. Goodfellow
8. Christian Szegedy
9. Nicolas Papernot
10. Nicholas Carlini

Who trusts Minhao Cheng overall

1. Yinpeng Dong
2. Jianbo Chen
3. Thomas A. Hogan
4. David Stutz
5. W. Zhang
6. Naveed Akhtar
7. Tianxing He
8. Huan Zhang
9. Xuanqing Liu
10. Adnan Siraj Rakin

Papers from Minhao Cheng's perspective

Ordered by how strongly the researchers Minhao Cheng trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples (2018) — 268 citations
• Query-Efficient Hard-label Black-box Attack: An Optimization-based Approach (2018) — 383 citations
• Towards Robust Neural Networks via Random Self-ensemble (2017) — 462 citations
• On the Robustness of Self-Attentive Models (2019) — 110 citations