Xiang Zhou

Artificial Intelligence · with Bansal · 2020

A researcher in the Science corpus: 2 papers; trusts 160 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 64 citations into it; #22,644 of 35,345 by trust here, #21,316 by citations
• Natural Language Processing — 54 citations into it; #3,799 of 5,688 by trust here, #4,112 by citations
• Evaluation & Benchmarks — 27 citations into it
• Computer Vision and Pattern Recognition — 26 citations into it; #14,028 of 21,575 by trust here, #14,151 by citations
• Safety, Robustness & Interpretability — 14 citations into it; #1,486 of 2,251 by trust here, #2,161 by citations
• Large Language Models — 8 citations into it
• Representation & Self-Supervised Learning — 6 citations into it

Whom Xiang Zhou trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Jacob Devlin
5. Ilya Sutskever
6. Alex Krizhevsky
7. Samuel R. Bowman
8. Jieyu Zhao
9. Robin Jia
10. Yann LeCun

Who trusts Xiang Zhou overall

1. Yuxiang Wu
2. John Miller
3. Matt Gardner
4. Tanya Goyal
5. A. D'Amour
6. Shanya Sharma Sharma
7. Pontus Stenetorp

Papers from Xiang Zhou's perspective

Ordered by how strongly the researchers Xiang Zhou 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. Attention is All you Need (Neural Information Processing Systems)
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. ImageNet: A large-scale hierarchical image database
8. Gradient-based learning applied to document recognition
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• The Curse of Performance Instability in Analysis Datasets: Consequences, Source, and Suggestions (2020) — 42 citations
• Towards Robustifying NLI Models Against Lexical Dataset Biases (2020) — 62 citations