Rui Zhang

Artificial Intelligence · Annual Meeting of the Association for Computational Linguistics · 2016–2024

A researcher in the Science corpus: 20 papers; trusts 492 researchers and is trusted by 61 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 723 citations into it; #3,945 of 35,345 by trust here, #2,269 by citations
• Natural Language Processing — 584 citations into it; #1,234 of 5,688 by trust here, #662 by citations
• Computer Vision and Pattern Recognition — 221 citations into it
• Evaluation & Benchmarks — 126 citations into it
• Large Language Models — 107 citations into it
• Information Systems — 93 citations into it; #1,773 of 8,771 by trust here, #312 by citations
• Model Architectures — 63 citations into it
• Natural Language Generation — 61 citations into it

Whom Rui Zhang trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Ilya Sutskever
4. Geoffrey E. Hinton
5. Richard Socher
6. Oriol Vinyals
7. Sepp Hochreiter
8. Alex Krizhevsky
9. Ronald J. Williams
10. Tomas Mikolov

Who trusts Rui Zhang overall

1. Tao Yu
2. Michihiro Yasunaga
3. Zhuang Li
4. Timo Schick
5. Ahmed Elgohary
6. Xinyun Chen
7. Percy Liang
8. Hinrich Schütze
9. Victor Zhong
10. Ruiqi Zhong

Papers from Rui Zhang's perspective

Ordered by how strongly the researchers Rui Zhang 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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task (2018) — 1,963 citations
• Improving Text-to-SQL Evaluation Methodology (2018) — 311 citations
• UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models (2022) — 362 citations
• Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions (2019) — 161 citations
• TypeSQL: Knowledge-Based Type-Aware Neural Text-to-SQL Generation (2018) — 308 citations
• DART: Open-Domain Structured Data Record to Text Generation (2020) — 230 citations
• CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases (2019) — 312 citations
• SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-Domain Text-to-SQL Task (2018) — 229 citations
• SParC: Cross-Domain Semantic Parsing in Context (2019) — 251 citations
• Graph-based Neural Multi-Document Summarization (2017) — 280 citations
• Selective Annotation Makes Language Models Better Few-Shot Learners (2022) — 329 citations
• FeTaQA: Free-form Table Question Answering (2021) — 274 citations
• Logic-Consistency Text Generation from Semantic Parses (2021) — 38 citations
• This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation (2019) — 85 citations
• Large Language Models for Mathematical Reasoning: Progresses and Challenges (2024) — 349 citations
• BLOOM: A 176B-Parameter Open-Access Multilingual Language Model (2022) — 2,975 citations
• Prefix-to-SQL: Text-to-SQL Generation from Incomplete User Questions (2021) — 5 citations
• SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing (2021) — 75 citations
• ESPRIT: Explaining Solutions to Physical Reasoning Tasks (2020) — 29 citations
• Dependency Sensitive Convolutional Neural Networks for Modeling Sentences and Documents (2016) — 132 citations