Ninghao Liu

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 7 papers; trusts 574 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 259 citations into it; #6,800 of 35,345 by trust here, #8,706 by citations
• Computer Vision and Pattern Recognition — 103 citations into it; #9,355 of 21,575 by trust here, #14,478 by citations
• Natural Language Processing — 83 citations into it
• Safety, Robustness & Interpretability — 82 citations into it
• Representation & Self-Supervised Learning — 36 citations into it
• Statistical and Nonlinear Physics — 34 citations into it
• Graph Learning — 30 citations into it
• Information Systems — 29 citations into it

Whom Ninghao Liu trusts overall

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

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

Who trusts Ninghao Liu overall

1. Mengnan Du
2. Yongfeng Zhang
3. Sina Mohseni
4. D. Gunning
5. Xia Hu
6. Xinyi Zhou
7. Xu Chen
8. Ziqi Yang
9. Hao Yuan
10. Qingquan Song

Papers from Ninghao Liu's perspective

Ordered by how strongly the researchers Ninghao Liu 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. Learning Multiple Layers of Features from Tiny Images

Their papers

• Techniques for interpretable machine learning (2018) — 1,244 citations
• An Interpretable Classification Framework for Information Extraction from Online Healthcare Forums (2017) — 29 citations
• Explainability for Large Language Models: A Survey (2023) — 887 citations
• Learning Credible Deep Neural Networks with Rationale Regularization (2019) — 47 citations
• Deep Representation Learning for Social Network Analysis (2019) — 116 citations
• Towards Explanation of DNN-based Prediction with Guided Feature Inversion (2018) — 134 citations
• Contextual Outlier Interpretation (2017) — 76 citations