Bing Tian Dai
Natural Language Processing · 2018–2021
A researcher in the Science corpus: 4 papers; trusts 104 researchers and is trusted by 18 researchers here, as inferred from the citation graph.
Subfields
- Natural Language Processing — 137 citations into it; #1,959 of 5,688 by trust here, #2,069 by citations
- Artificial Intelligence — 133 citations into it; #10,389 of 35,345 by trust here, #10,319 by citations
- Computer Vision and Pattern Recognition — 36 citations into it; #18,890 of 21,575 by trust here, #13,815 by citations
- Information Systems — 31 citations into it
- Computational Theory and Mathematics — 14 citations into it
- Reinforcement Learning — 13 citations into it
- Evaluation & Benchmarks — 11 citations into it
- Prompting & Reasoning — 10 citations into it
Whom Bing Tian Dai trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Subhro Roy
- Lei Wang
- Ilya Sutskever
- Yoshua Bengio
- Danqing Huang
- Geoffrey E. Hinton
- Richard Socher
- Dan Roth
- David G. Lowe
- Diederik P. Kingma
Who trusts Bing Tian Dai overall
Papers from Bing Tian Dai's perspective
Ordered by how strongly the researchers Bing Tian Dai trusts endorse each paper. Same corpus, a different order for every researcher.
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- ImageNet: A large-scale hierarchical image database
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
Their papers
- The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers (2018) — 149 citations
- Template-Based Math Word Problem Solvers with Recursive Neural Networks (2019) — 168 citations
- MWPToolkit: An Open-Source Framework for Deep Learning-Based Math Word Problem Solvers (2021) — 79 citations
- Modeling Intra-Relation in Math Word Problems with Different Functional Multi-Head Attentions (2019) — 109 citations