Jiangming Liu

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 5 papers; trusts 194 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 179 citations into it; #10,479 of 35,345 by trust here, #7,797 by citations
• Natural Language Processing — 162 citations into it; #2,066 of 5,688 by trust here, #3,053 by citations
• Evaluation & Benchmarks — 65 citations into it
• Computer Vision and Pattern Recognition — 64 citations into it
• Model Architectures — 12 citations into it

Whom Jiangming Liu trusts overall

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

1. Diederik P. Kingma
2. Michael Collins
3. Christopher D. Manning
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Jimmy Ba
7. Ilya Sutskever
8. Sepp Hochreiter
9. Alex Krizhevsky
10. Danqi Chen

Who trusts Jiangming Liu overall

1. Daniel Fried
2. Kevin Clark
3. Laria Reynolds
4. Nikita Kitaev
5. Daniel Klein
6. Minh-Thang Luong
7. Kyle McDonell
8. Christopher D. Manning
9. Banghao Chen
10. Amirsina Torfi

Papers from Jiangming Liu's perspective

Ordered by how strongly the researchers Jiangming 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. ImageNet: A large-scale hierarchical image database
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

• Evaluating Models’ Local Decision Boundaries via Contrast Sets (2020) — 462 citations
• Evaluating NLP Models via Contrast Sets (2020) — 85 citations
• In-Order Transition-based Constituent Parsing (2017) — 79 citations
• Multi-Step Inference for Reasoning over Paragraphs (2020) — 21 citations
• Discourse Representation Structure Parsing (2018) — 49 citations