Kun He

Artificial Intelligence · Annual Meeting of the Association for Computational Linguistics · 2016–2019

A researcher in the Science corpus: 6 papers; trusts 486 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 129 citations into it; #13,761 of 35,345 by trust here, #9,870 by citations
• Computer Vision and Pattern Recognition — 113 citations into it
• Safety, Robustness & Interpretability — 75 citations into it
• Signal Processing — 20 citations into it; #3,190 of 7,572 by trust here, #3,673 by citations
• Multimodal Learning — 19 citations into it
• Natural Language Processing — 19 citations into it
• Representation & Self-Supervised Learning — 19 citations into it
• Model Architectures — 18 citations into it

Whom Kun He trusts overall

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

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

Who trusts Kun He overall

1. Eunbyung Park
2. Anqi Mao
3. Jie Lei
4. Liwei Song
5. Jimei Yang
6. Jonghwan Mun
7. Adam Gaier
8. Zonghan Yang
9. Naveed Akhtar
10. Siddhant Garg

Papers from Kun He's perspective

Ordered by how strongly the researchers Kun He 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. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Multilevel Language and Vision Integration for Text-to-Clip Retrieval (2018) — 369 citations
• Adversarially Robust Generalization Just Requires More Unlabeled Data (2019) — 161 citations
• A Powerful Generative Model Using Random Weights for the Deep Image Representation (2016) — 79 citations
• Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency (2019) — 822 citations
• Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks (2019) — 796 citations
• Improving the Generalization of Adversarial Training with Domain Adaptation (2018) — 148 citations