Chenyan Jia

Artificial Intelligence · 2020–2023

A researcher in the Science corpus: 4 papers; trusts 105 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 123 citations into it; #26,235 of 35,345 by trust here, #19,873 by citations
• Natural Language Processing — 94 citations into it; #4,345 of 5,688 by trust here, #4,361 by citations
• Computer Vision and Pattern Recognition — 40 citations into it
• Large Language Models — 37 citations into it
• Safety, Robustness & Interpretability — 32 citations into it
• Sociology and Political Science — 19 citations into it
• Representation & Self-Supervised Learning — 12 citations into it
• Reinforcement Learning — 11 citations into it

Whom Chenyan Jia 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. Kenneth Heafield
6. Alex Krizhevsky
7. Tomas Mikolov
8. Yann LeCun
9. Kaiming He
10. Ruslan Salakhutdinov

Who trusts Chenyan Jia overall

1. Ruibo Liu
2. Hao Sun
3. Guangxuan Xu

Papers from Chenyan Jia's perspective

Ordered by how strongly the researchers Chenyan Jia 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. 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Data Boost: Text Data Augmentation through Reinforcement Learning Guided Conditional Generation (2020) — 115 citations
• A Transformer-based Framework for Neutralizing and Reversing the Political Polarity of News Articles (2021) — 34 citations
• Second Thoughts are Best: Learning to Re-Align With Human Values from Text Edits (2023) — 44 citations
• Mitigating Political Bias in Language Models Through Reinforced Calibration (2021) — 116 citations