Ning Xie

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 4 papers; trusts 667 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 231 citations into it; #9,276 of 35,345 by trust here, #9,465 by citations
• Computer Vision and Pattern Recognition — 149 citations into it; #6,540 of 21,575 by trust here, #7,499 by citations
• Safety, Robustness & Interpretability — 117 citations into it
• Natural Language Processing — 67 citations into it
• Multimodal Learning — 34 citations into it; #344 of 1,211 by trust here, #610 by citations
• Evaluation & Benchmarks — 24 citations into it
• Model Architectures — 24 citations into it
• Safety Research — 17 citations into it

Whom Ning Xie trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Diederik P. Kingma
6. Ross B. Girshick
7. Ilya Sutskever
8. Yann LeCun
9. Oriol Vinyals
10. Karen Simonyan

Who trusts Ning Xie overall

1. Ronghang Hu
2. Sarah Wiegreffe
3. Zhe Gan
4. Yen-Chun Chen
5. Ana Marasovic
6. Virginie Do
7. Oana-Maria Camburu
8. Linjie Li
9. Amanpreet Singh
10. Bodhisattwa Prasad Majumder

Papers from Ning Xie's perspective

Ordered by how strongly the researchers Ning Xie 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Visual Entailment: A Novel Task for Fine-Grained Image Understanding (2019) — 367 citations
• Explainable Deep Learning: A Field Guide for the Uninitiated (2020) — 452 citations
• Visual Entailment Task for Visually-Grounded Language Learning (2018) — 60 citations
• Explaining Trained Neural Networks with Semantic Web Technologies: First Steps (2017) — 69 citations