Yong Cheng

Artificial Intelligence · with Liu · 2014–2022

A researcher in the Science corpus: 12 papers; trusts 306 researchers and is trusted by 61 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 188 citations into it; #3,908 of 35,345 by trust here, #3,348 by citations
• Natural Language Processing — 138 citations into it; #927 of 5,688 by trust here, #836 by citations
• Computer Vision and Pattern Recognition — 100 citations into it; #5,251 of 21,575 by trust here, #3,393 by citations
• Model Architectures — 33 citations into it
• Optimization & Training — 25 citations into it
• Safety, Robustness & Interpretability — 25 citations into it; #948 of 2,251 by trust here, #1,249 by citations
• Evaluation & Benchmarks — 19 citations into it
• Machine Translation — 13 citations into it

Whom Yong Cheng trusts overall

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

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

Who trusts Yong Cheng overall

1. Yang Liu
2. Yun Chen
3. Lu Jiang
4. Linyang Li
5. Zhenhao Li
6. Jiatao Gu
7. Bohan Li
8. Felix Stahlberg
9. Zhaopeng Tu
10. Sufeng Duan

Papers from Yong Cheng's perspective

Ordered by how strongly the researchers Yong Cheng 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Minimum Risk Training for Neural Machine Translation (2015) — 480 citations
• Semi-Supervised Learning for Neural Machine Translation (2016) — 261 citations
• A Teacher-Student Framework for Zero-Resource Neural Machine Translation (2017) — 156 citations
• AdvAug: Robust Adversarial Augmentation for Neural Machine Translation (2020) — 129 citations
• Robust Neural Machine Translation with Doubly Adversarial Inputs (2019) — 269 citations
• Towards Robust Neural Machine Translation (2018) — 167 citations
• Topologically-associating domains are stable units of replication-timing regulation (2014) — 841 citations
• FedBCD: A Communication-Efficient Collaborative Learning Framework for Distributed Features (2022) — 139 citations
• Electrolyte Engineering Enables High Stability and Capacity Alloying Anodes for Sodium and Potassium Ion Batteries (2020) — 179 citations
• Improving predicted mean vote with inversely determined metabolic rate (2020) — 62 citations
• A Communication Efficient Vertical Federated Learning Framework (2019) — 79 citations
• Neural Machine Translation with Pivot Languages (2016) — 35 citations