Hongge Chen

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 9 papers; trusts 314 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 248 citations into it; #13,282 of 35,345 by trust here, #5,788 by citations
• Safety, Robustness & Interpretability — 151 citations into it; #1,131 of 2,251 by trust here, #611 by citations
• Computer Vision and Pattern Recognition — 119 citations into it; #18,053 of 21,575 by trust here, #8,612 by citations
• Natural Language Processing — 37 citations into it
• Reinforcement Learning — 34 citations into it
• Multimodal Learning — 32 citations into it; #1,193 of 1,211 by trust here, #831 by citations
• Hardware and Architecture — 27 citations into it
• Signal Processing — 26 citations into it

Whom Hongge Chen trusts overall

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

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

Who trusts Hongge Chen overall

1. Huan Zhang
2. Tsui-Wei Weng
3. Haifeng Qian
4. Naveed Akhtar
5. W. Zhang
6. Yinfei Yang
7. Kaidi Xu
8. Alexander Warnecke
9. Yan Xu
10. Tianxing He

Papers from Hongge Chen's perspective

Ordered by how strongly the researchers Hongge Chen 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Is Robustness the Cost of Accuracy? - A Comprehensive Study on the Robustness of 18 Deep Image Classification Models (2018) — 421 citations
• Towards Fast Computation of Certified Robustness for ReLU Networks (2018) — 755 citations
• Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning (2017) — 137 citations
• Towards Stable and Efficient Training of Verifiably Robust Neural Networks (2019) — 393 citations
• Multi-Stage Influence Function (2020) — 21 citations
• Adversarial T-Shirt! Evading Person Detectors in a Physical World (2019) — 428 citations
• Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective (2019) — 538 citations
• Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations (2020) — 405 citations
• Show-and-Fool: Crafting Adversarial Examples for Neural Image Captioning (2017) — 50 citations