Jinsong Su

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 6 papers; trusts 177 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 129 citations into it; #10,467 of 35,345 by trust here, #8,999 by citations
• Natural Language Processing — 110 citations into it; #3,676 of 5,688 by trust here, #2,888 by citations
• Computer Vision and Pattern Recognition — 60 citations into it; #8,239 of 21,575 by trust here, #7,340 by citations
• Model Architectures — 45 citations into it; #1,161 of 2,278 by trust here, #1,693 by citations
• Evaluation & Benchmarks — 13 citations into it
• Generative Models — 8 citations into it
• Optimization & Training — 7 citations into it

Whom Jinsong Su trusts overall

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

1. Yoshua Bengio
2. Alex Graves
3. Kyunghyun Cho
4. Ilya Sutskever
5. Diederik P. Kingma
6. Philipp Koehn
7. Geoffrey E. Hinton
8. Dzmitry Bahdanau
9. Sepp Hochreiter
10. J. Urgen Schmidhuber

Who trusts Jinsong Su overall

1. Jie Yang
2. Jacob Buckman
3. Graham Neubig
4. Matthias Sperber
5. Angela Fan
6. Fengshun Xiao
7. Jiali Zeng
8. Felix Stahlberg
9. Biao Zhang
10. Van-Khanh Tran

Papers from Jinsong Su's perspective

Ordered by how strongly the researchers Jinsong Su 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
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

• Variational Neural Machine Translation (2016) — 209 citations
• Accelerating Neural Transformer via an Average Attention Network (2018) — 128 citations
• Structural Information Preserving for Graph-to-Text Generation (2020) — 58 citations
• Multi-Domain Neural Machine Translation with Word-Level Domain Context Discrimination (2018) — 79 citations
• Lattice-Based Recurrent Neural Network Encoders for Neural Machine Translation (2016) — 92 citations
• Neural Machine Translation With GRU-Gated Attention Model (2020) — 110 citations