Zhijing Jin

Artificial Intelligence · with Jin · 2019–2020

A researcher in the Science corpus: 4 papers; trusts 195 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 195 citations into it; #16,874 of 35,345 by trust here, #10,430 by citations
• Natural Language Processing — 155 citations into it
• Computer Vision and Pattern Recognition — 81 citations into it
• Safety, Robustness & Interpretability — 37 citations into it; #726 of 2,251 by trust here, #849 by citations
• Representation & Self-Supervised Learning — 22 citations into it
• Evaluation & Benchmarks — 21 citations into it
• Large Language Models — 11 citations into it
• Model Architectures — 9 citations into it

Whom Zhijing Jin trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Ilya Sutskever
5. Yann LeCun
6. Alex Krizhevsky
7. Sepp Hochreiter
8. Yoon Kim
9. Richard Socher
10. Ruslan Salakhutdinov

Who trusts Zhijing Jin overall

1. Linyang Li
2. Di Jin
3. Adyasha Maharana
4. Siddhant Garg
5. Eleftheria Briakou
6. M. V. Koroteev
7. Zhiqiang Hu
8. Xipeng Qiu
9. Ning Dai
10. Leo Laugier

Papers from Zhijing Jin's perspective

Ordered by how strongly the researchers Zhijing Jin 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment (2019) — 1,382 citations
• Deep Learning for Text Style Transfer: A Survey (2020) — 303 citations
• IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation (2019) — 77 citations
• Is BERT Really Robust? Natural Language Attack on Text Classification and Entailment (2019)