Zhucheng Tu

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 4 papers; trusts 49 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 65 citations into it; #34,690 of 35,345 by trust here, #19,018 by citations
• Natural Language Processing — 53 citations into it; #5,559 of 5,688 by trust here, #3,826 by citations
• Computer Vision and Pattern Recognition — 34 citations into it; #21,144 of 21,575 by trust here, #12,992 by citations
• Evaluation & Benchmarks — 26 citations into it
• Information Retrieval & Search — 21 citations into it
• Large Language Models — 11 citations into it
• Information Systems — 10 citations into it

Whom Zhucheng Tu trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Ellen M. Voorhees
4. Leif Azzopardi
5. Diederik P. Kingma
6. Aliaksei Severyn
7. Ilya Sutskever
8. Jacob Devlin
9. Yann LeCun
10. Alex Krizhevsky

Who trusts Zhucheng Tu overall

1. Thiago Akio Nakamura
2. Jinhyuk Lee
3. Shayne Longpre

Papers from Zhucheng Tu's perspective

Ordered by how strongly the researchers Zhucheng Tu trusts endorse each paper. Same corpus, a different order for every researcher.

1. Attention is All you Need (Neural Information Processing Systems)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Gradient-based learning applied to document recognition
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Open-Domain Question Answering Goes Conversational via Question Rewriting (2020) — 230 citations
• An Exploration of Data Augmentation and Sampling Techniques for Domain-Agnostic Question Answering (2019) — 74 citations
• A Wrong Answer or a Wrong Question? An Intricate Relationship between Question Reformulation and Answer Selection in Conversational Question Answering (2020) — 14 citations
• An Exploration of Approaches to Integrating Neural Reranking Models in Multi-Stage Ranking Architectures (2017) — 7 citations