Jingbo Zhu

Artificial Intelligence · 2004–2022

A researcher in the Science corpus: 14 papers; trusts 303 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 191 citations into it; #9,871 of 35,345 by trust here, #6,848 by citations
• Natural Language Processing — 136 citations into it; #2,345 of 5,688 by trust here, #1,636 by citations
• Computer Vision and Pattern Recognition — 131 citations into it; #20,548 of 21,575 by trust here, #5,680 by citations
• Model Architectures — 74 citations into it
• Optimization & Training — 32 citations into it
• Efficiency & Compression — 18 citations into it
• Multimodal Learning — 15 citations into it
• Evaluation & Benchmarks — 10 citations into it

Whom Jingbo Zhu trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Diederik P. Kingma
5. Andrew McCallum
6. Thomas G. Dietterich
7. Ilya Sutskever
8. Alex Krizhevsky
9. Jimmy Ba
10. Thorsten Joachims

Who trusts Jingbo Zhu overall

1. Bei Li
2. Tiago A. Almeida
3. Tong Xiao
4. Qiang Wang
5. Thiago S. Guzella
6. B. Shravan Kumar
7. Loet Leydesdorff
8. Enrico Blanzieri
9. Giorgio Fumera
10. José María Gómez Hidalgo

Papers from Jingbo Zhu's perspective

Ordered by how strongly the researchers Jingbo Zhu 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Learning Deep Transformer Models for Machine Translation (2019) — 775 citations
• The NiuTrans Machine Translation Systems for WMT19 (2019) — 59 citations
• Active Learning with Sampling by Uncertainty and Density for Word Sense Disambiguation and Text Classification (2008) — 219 citations
• On Vision Features in Multimodal Machine Translation (2022) — 85 citations
• An Efficient Transformer Decoder with Compressed Sub-layers (2021) — 33 citations
• Towards Fully 8-bit Integer Inference for the Transformer Model (2020) — 67 citations
• Learning Light-Weight Translation Models from Deep Transformer (2020) — 44 citations
• Improved Differentiable Architecture Search for Language Modeling and Named Entity Recognition (2019) — 83 citations
• Sharing Attention Weights for Fast Transformer (2019) — 62 citations
• The NiuTrans Machine Translation System for WMT18 (2018) — 13 citations
• Multi-layer Representation Fusion for Neural Machine Translation (2018) — 55 citations
• Fast and Accurate Shift-Reduce Constituent Parsing (2013) — 221 citations
• NiuTrans: An Open Source Toolkit for Phrase-based and Syntax-based Machine Translation (2012) — 81 citations
• An evaluation of statistical spam filtering techniques (2004) — 362 citations