Tony Sun
Artificial Intelligence · 2019–2021
A researcher in the Science corpus: 5 papers; trusts 36 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 177 citations into it
- Natural Language Processing — 146 citations into it
- Safety, Robustness & Interpretability — 81 citations into it
Whom Tony Sun trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Jieyu Zhao
- Geoffrey E. Hinton
- Yoshua Bengio
- Ilya Sutskever
- Jimmy Ba
- Alex Krizhevsky
- Kenton C. T. Lee
- Tomas Mikolov
- Yann LeCun
Who trusts Tony Sun overall
- Karolina Stańczak
- Sunipa Dev
- Eduard H. Hovy
- Anjalie Field
- Emily Sheng
- Isabel O. Gallegos
- Prasanna Parasurama
- Zahra Fatemi
- Percy Liang
- Beatrice Savoldi
Papers from Tony Sun's perspective
Ordered by how strongly the researchers Tony Sun trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet: A large-scale hierarchical image database
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
Their papers
- Mitigating Gender Bias in Natural Language Processing: Literature Review (2019) — 648 citations
- BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation (2021) — 594 citations
- Towards Understanding Gender Bias in Relation Extraction (2019) — 44 citations
- They, Them, Theirs: Rewriting with Gender-Neutral English (2021) — 66 citations
- NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation (2021) — 104 citations