Yingce Xia

Artificial Intelligence · 2016–2022

A researcher in the Science corpus: 20 papers; trusts 781 researchers and is trusted by 33 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 472 citations into it; #6,926 of 35,345 by trust here, #2,070 by citations
• Natural Language Processing — 346 citations into it; #1,418 of 5,688 by trust here, #503 by citations
• Computer Vision and Pattern Recognition — 301 citations into it; #5,998 of 21,575 by trust here, #1,836 by citations
• Model Architectures — 119 citations into it; #2,178 of 2,278 by trust here, #1,433 by citations
• Optimization & Training — 64 citations into it
• Evaluation & Benchmarks — 50 citations into it
• Large Language Models — 33 citations into it
• Representation & Self-Supervised Learning — 29 citations into it

Whom Yingce Xia trusts overall

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

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

Who trusts Yingce Xia overall

1. Lijun Wu
2. Tao Qin
3. Tao Ge
4. Fei Tian
5. Xu Tan
6. Furu Wei
7. Yijun Wang
8. Yiren Wang
9. Tie-Yan Liu
10. Di He

Papers from Yingce Xia's perspective

Ordered by how strongly the researchers Yingce Xia 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Dual Learning for Machine Translation (2016) — 900 citations
• Achieving Human Parity on Automatic Chinese to English News Translation (2018) — 645 citations
• Dual Supervised Learning (2017) — 148 citations
• Microsoft Research Asia’s Systems for WMT19 (2019) — 26 citations
• Adversarial Neural Machine Translation (2017) — 137 citations
• Dual Transfer Learning for Neural Machine Translation with Marginal Distribution Regularization (2018) — 66 citations
• Multilingual Neural Machine Translation with Language Clustering (2019) — 124 citations
• BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining (2022) — 1,456 citations
• Depth Growing for Neural Machine Translation (2019) — 45 citations
• Learning to Teach with Dynamic Loss Functions (2018) — 126 citations
• Soft Contextual Data Augmentation for Neural Machine Translation (2019) — 136 citations
• Learning to Reweight with Deep Interactions (2021) — 6 citations
• Searching Better Architectures for Neural Machine Translation (2020) — 27 citations
• Incorporating BERT into Neural Machine Translation (2020)
• Multi-Agent Dual Learning (2019) — 62 citations
• Efficient Bidirectional Neural Machine Translation (2019) — 5 citations
• Exploiting Monolingual Data at Scale for Neural Machine Translation (2019) — 72 citations
• Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation (2018) — 125 citations
• Deliberation Networks: Sequence Generation Beyond One-Pass Decoding (2017) — 221 citations
• Decoding with Value Networks for Neural Machine Translation (2017) — 53 citations