Biao Zhang

Artificial Intelligence · with Li · 2016–2024

A researcher in the Science corpus: 10 papers; trusts 698 researchers and is trusted by 50 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 259 citations into it; #4,809 of 35,345 by trust here, #3,715 by citations
• Natural Language Processing — 189 citations into it; #2,394 of 5,688 by trust here, #2,156 by citations
• Computer Vision and Pattern Recognition — 152 citations into it; #5,747 of 21,575 by trust here, #5,287 by citations
• Model Architectures — 64 citations into it; #654 of 2,278 by trust here, #842 by citations
• Optimization & Training — 33 citations into it; #1,139 of 2,601 by trust here, #1,022 by citations
• Evaluation & Benchmarks — 28 citations into it
• Large Language Models — 24 citations into it
• Efficiency & Compression — 11 citations into it; #617 of 1,264 by trust here, #968 by citations

Whom Biao Zhang trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Jimmy Ba
5. Alex Krizhevsky
6. Rico Sennrich
7. Kyunghyun Cho
8. Ilya Sutskever
9. Philipp Koehn
10. Alex Graves

Who trusts Biao Zhang overall

1. Angela Fan
2. Xian Li
3. Markus Freitag
4. Adam Gleave
5. Van-Khanh Tran
6. Yiming Cui
7. Luca Soldaini
8. Rico Sennrich
9. Yuqing Tang
10. Orhan Firat

Papers from Biao Zhang's perspective

Ordered by how strongly the researchers Biao Zhang 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

• Root Mean Square Layer Normalization (2019) — 1,797 citations
• Variational Neural Machine Translation (2016) — 209 citations
• Accelerating Neural Transformer via an Average Attention Network (2018) — 128 citations
• Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation (2020) — 456 citations
• Revisiting Low-Resource Neural Machine Translation: A Case Study (2019) — 237 citations
• Share or Not? Learning to Schedule Language-Specific Capacity for Multilingual Translation (2021) — 97 citations
• Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024) — 3,818 citations
• Prompting Large Language Model for Machine Translation: A Case Study (2023) — 424 citations
• Neural Machine Translation With GRU-Gated Attention Model (2020) — 110 citations
• A Lightweight Recurrent Network for Sequence Modeling (2019) — 9 citations