Zhongjun He

Artificial Intelligence · 2015–2019

A researcher in the Science corpus: 7 papers; trusts 101 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 95 citations into it; #6,932 of 35,345 by trust here, #4,336 by citations
• Natural Language Processing — 88 citations into it; #1,576 of 5,688 by trust here, #1,161 by citations
• Computer Vision and Pattern Recognition — 40 citations into it; #5,834 of 21,575 by trust here, #3,680 by citations
• Model Architectures — 26 citations into it
• Evaluation & Benchmarks — 9 citations into it
• Optimization & Training — 8 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Zhongjun He trusts overall

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

1. Franz Joseph Och
2. Kyunghyun Cho
3. Dzmitry Bahdanau
4. Yoshua Bengio
5. Ilya Sutskever
6. Geoffrey E. Hinton
7. Andreas Stolcke
8. Philipp Koehn
9. Diederik P. Kingma
10. Matthew D. Zeiler

Who trusts Zhongjun He overall

1. Felix Stahlberg
2. Yong Cheng
3. Zhaopeng Tu
4. Kenton Murray
5. Yang Liu
6. Lijun Wu
7. Julia Kreutzer
8. Isaac Caswell
9. Yingce Xia
10. Clara Meister

Papers from Zhongjun He's perspective

Ordered by how strongly the researchers Zhongjun He 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. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Minimum Risk Training for Neural Machine Translation (2015) — 480 citations
• Semi-Supervised Learning for Neural Machine Translation (2016) — 261 citations
• Robust Neural Machine Translation with Joint Textual and Phonetic Embedding (2018) — 60 citations
• Improved Neural Machine Translation with SMT Features (2016) — 123 citations
• STACL: Simultaneous Translation with Implicit Anticipation and Controllable Latency using Prefix-to-Prefix Framework (2018) — 323 citations
• STACL: Simultaneous Translation with Integrated Anticipation and Controllable Latency (2018) — 31 citations
• Synchronous Speech Recognition and Speech-to-Text Translation with Interactive Decoding (2019) — 73 citations