Mengnan Du

Artificial Intelligence · 2018–2023

A researcher in the Science corpus: 7 papers; trusts 707 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 282 citations into it; #7,396 of 35,345 by trust here, #7,346 by citations
• Computer Vision and Pattern Recognition — 149 citations into it; #9,832 of 21,575 by trust here, #10,677 by citations
• Safety, Robustness & Interpretability — 135 citations into it
• Natural Language Processing — 103 citations into it
• Model Architectures — 32 citations into it
• Biophysics — 17 citations into it
• Multimodal Learning — 17 citations into it
• Information Systems — 15 citations into it

Whom Mengnan Du trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Karen Simonyan
4. Alex Krizhevsky
5. Yoshua Bengio
6. Kaiming He
7. Yann LeCun
8. Ilya Sutskever
9. Andrew Zisserman
10. Jimmy Ba

Who trusts Mengnan Du overall

1. Sina Mohseni
2. Xinyi Zhou
3. Ziqi Yang
4. Hao Yuan
5. Xinru Wang
6. Fei Wang
7. Fan Yang
8. Xia Hu
9. Eric D. Ragan
10. D. Gunning

Papers from Mengnan Du's perspective

Ordered by how strongly the researchers Mengnan Du 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. 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

• Techniques for interpretable machine learning (2018) — 1,244 citations
• Explainability for Large Language Models: A Survey (2023) — 887 citations
• Mitigating Gender Bias in Captioning Systems (2020) — 78 citations
• Evaluating Explanation Without Ground Truth in Interpretable Machine Learning (2019) — 81 citations
• Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks (2019) — 1,511 citations
• Learning Credible Deep Neural Networks with Rationale Regularization (2019) — 47 citations
• Towards Explanation of DNN-based Prediction with Guided Feature Inversion (2018) — 134 citations