Tong Xiao
Artificial Intelligence · with Li · 2012–2022
A researcher in the Science corpus: 11 papers; trusts 266 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 172 citations into it; #33,676 of 35,345 by trust here, #7,657 by citations
- Computer Vision and Pattern Recognition — 130 citations into it; #20,547 of 21,575 by trust here, #5,680 by citations
- Natural Language Processing — 110 citations into it; #3,644 of 5,688 by trust here, #1,910 by citations
- Model Architectures — 72 citations into it
- Optimization & Training — 29 citations into it
- Efficiency & Compression — 18 citations into it
- Multimodal Learning — 15 citations into it
- Management Science and Operations Research — 5 citations into it
Whom Tong Xiao trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yoshua Bengio
- Ilya Sutskever
- Franz Joseph Och
- Alex Krizhevsky
- Jimmy Ba
- Ashish Vaswani
- Philipp Koehn
Who trusts Tong Xiao overall
- Bei Li
- Qiang Wang
- Ye Lin
- Luca Soldaini
- Jingbo Zhu
- Yinqiao Li
- Yanyang Li
- Ziyang Wang
- Po-Han Chi
- Alessandro Moschitti
Papers from Tong Xiao's perspective
Ordered by how strongly the researchers Tong Xiao trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Learning Deep Transformer Models for Machine Translation (2019) — 775 citations
- The NiuTrans Machine Translation Systems for WMT19 (2019) — 59 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
- NiuTrans: An Open Source Toolkit for Phrase-based and Syntax-based Machine Translation (2012) — 81 citations