Bailin Wang

Artificial Intelligence · with Li · 2019–2023

A researcher in the Science corpus: 6 papers; trusts 114 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 272 citations into it; #13,194 of 35,345 by trust here, #6,468 by citations
• Natural Language Processing — 185 citations into it; #2,491 of 5,688 by trust here, #1,753 by citations
• Computer Vision and Pattern Recognition — 80 citations into it
• Information Systems — 42 citations into it; #6,350 of 8,771 by trust here, #1,815 by citations
• Management Science and Operations Research — 21 citations into it
• Model Architectures — 19 citations into it
• Representation & Self-Supervised Learning — 16 citations into it
• Reinforcement Learning — 14 citations into it

Whom Bailin Wang trusts overall

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

1. Diederik P. Kingma
2. Sepp Hochreiter
3. Geoffrey E. Hinton
4. Jimmy Ba
5. Yoshua Bengio
6. Richard Socher
7. J. Urgen Schmidhuber
8. Ilya Sutskever
9. Christopher D. Manning
10. Alex Krizhevsky

Who trusts Bailin Wang overall

1. Xinyun Chen
2. Pengcheng Yin
3. Jonathan Herzig
4. Qian Liu
5. M. Pourreza
6. Zhuang Li
7. Tao Yu
8. Ahmed Elgohary
9. Graham Neubig
10. Jonathan Berant

Papers from Bailin Wang's perspective

Ordered by how strongly the researchers Bailin Wang 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
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

• RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers (2019) — 858 citations
• UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models (2022) — 362 citations
• Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programs (2019) — 51 citations
• GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing (2020) — 296 citations
• Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks (2023) — 353 citations
• Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs (2023) — 1,029 citations