Zhijian Ou

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 8 papers; trusts 507 researchers and is trusted by 49 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 130 citations into it; #3,333 of 35,345 by trust here, #11,072 by citations
• Natural Language Processing — 85 citations into it; #1,079 of 5,688 by trust here, #2,732 by citations
• Computer Vision and Pattern Recognition — 58 citations into it; #14,214 of 21,575 by trust here, #20,271 by citations
• Probabilistic & Bayesian Methods — 29 citations into it; #958 of 1,525 by trust here, #1,198 by citations
• Generative Models — 28 citations into it; #1,355 of 1,956 by trust here, #1,902 by citations
• Optimization & Training — 21 citations into it
• Large Language Models — 16 citations into it; #229 of 799 by trust here, #751 by citations
• Model Architectures — 14 citations into it; #1,026 of 2,278 by trust here, #2,078 by citations

Whom Zhijian Ou trusts overall

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

1. Diederik P. Kingma
2. Ilya Sutskever
3. Wojciech Zaremba
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Tomas Mikolov
7. Joshua Goodman
8. Stanley F. Chen
9. Jimmy Ba
10. Holger Schwenk

Who trusts Zhijian Ou overall

1. Anton Bakhtin
2. Yuntian Deng
3. Myle Ott
4. Arthur Szlam
5. Sam Gross
6. Kevin Clark
7. Marc'Aurelio Ranzato
8. Minh-Thang Luong
9. Quoc V. Le
10. Yilun Du

Papers from Zhijian Ou's perspective

Ordered by how strongly the researchers Zhijian Ou trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Trans-dimensional Random Fields for Language Modeling (2015) — 17 citations
• Learning Neural Trans-Dimensional Random Field Language Models with Noise-Contrastive Estimation (2017) — 19 citations
• Learning Neural Random Fields with Inclusive Auxiliary Generators (2018) — 31 citations
• Task-Oriented Dialog Systems that Consider Multiple Appropriate Responses under the Same Context (2019) — 198 citations
• Learning Trans-Dimensional Random Fields with Applications to Language Modeling (2018) — 30 citations
• Language modeling with neural trans-dimensional random fields (2017) — 17 citations
• Improved Training Of Neural Trans-Dimensional Random field Language Models with Dynamic Noise-Contrastive Estimation (2018) — 15 citations
• Paraphrase Augmented Task-Oriented Dialog Generation (2020) — 92 citations