Sanghyun Hong

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 4 papers; trusts 188 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 93 citations into it
• Safety, Robustness & Interpretability — 57 citations into it
• Computer Vision and Pattern Recognition — 45 citations into it
• Computer Networks and Communications — 19 citations into it
• Efficiency & Compression — 16 citations into it
• Signal Processing — 16 citations into it
• Optimization & Training — 13 citations into it; #2,476 of 2,601 by trust here, #1,986 by citations
• Model Architectures — 11 citations into it; #2,037 of 2,278 by trust here, #1,882 by citations

Whom Sanghyun Hong trusts overall

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

1. Alex Krizhevsky
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Kaiming He
5. Christian Szegedy
6. Yoshua Bengio
7. Yann LeCun
8. Ian J. Goodfellow
9. Ilya Sutskever
10. Karen Simonyan

Who trusts Sanghyun Hong overall

1. Yizeng Han
2. Simone Scardapane
3. Stefanos Laskaridis
4. Xipeng Qiu
5. Sanyuan Chen
6. Wangchunshu Zhou
7. Canwen Xu

Papers from Sanghyun Hong's perspective

Ordered by how strongly the researchers Sanghyun Hong 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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

• On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping (2020) — 157 citations
• A Panda? No, It's a Sloth: Slowdown Attacks on Adaptive Multi-Exit Neural Network Inference (2020) — 88 citations
• Shallow-Deep Networks: Understanding and Mitigating Network Overthinking (2018) — 395 citations
• Summoning Demons: The Pursuit of Exploitable Bugs in Machine Learning (2017) — 20 citations