Shuhao Gu

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 6 papers; trusts 268 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 155 citations into it; #26,048 of 35,345 by trust here, #23,077 by citations
• Natural Language Processing — 128 citations into it
• Computer Vision and Pattern Recognition — 107 citations into it; #15,992 of 21,575 by trust here, #15,162 by citations
• Model Architectures — 32 citations into it
• Efficiency & Compression — 11 citations into it
• Evaluation & Benchmarks — 10 citations into it
• Machine Translation — 7 citations into it

Whom Shuhao Gu trusts overall

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

1. Rico Sennrich
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Ilya Sutskever
5. Minh-Thang Luong
6. Dzmitry Bahdanau
7. Kyunghyun Cho
8. Ashish Vaswani
9. Diederik P. Kingma
10. Christopher D. Manning

Who trusts Shuhao Gu overall

1. Yang Feng
2. Chenze Shao

Papers from Shuhao Gu's perspective

Ordered by how strongly the researchers Shuhao Gu 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. Attention is All you Need (Neural Information Processing Systems)
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
7. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. ImageNet: A large-scale hierarchical image database
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Importance-based Neuron Allocation for Multilingual Neural Machine Translation (2021) — 42 citations
• Pruning-then-Expanding Model for Domain Adaptation of Neural Machine Translation (2021) — 33 citations
• Token-level Adaptive Training for Neural Machine Translation (2020) — 39 citations
• Investigating Catastrophic Forgetting During Continual Training for Neural Machine Translation (2020) — 29 citations
• Improving Domain Adaptation Translation with Domain Invariant and Specific Information (2019) — 41 citations
• Modeling Fluency and Faithfulness for Diverse Neural Machine Translation (2019) — 28 citations