Guangxuan Xu

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 4 papers; trusts 33 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 108 citations into it
• Natural Language Processing — 89 citations into it
• Safety, Robustness & Interpretability — 49 citations into it
• Computer Vision and Pattern Recognition — 28 citations into it
• Large Language Models — 25 citations into it
• Representation & Self-Supervised Learning — 11 citations into it
• Communication — 8 citations into it

Whom Guangxuan Xu trusts overall

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

1. Sosuke Kobayashi
2. Geoffrey E. Hinton
3. Ilya Sutskever
4. Yoshua Bengio
5. Jason Wei
6. Yann LeCun
7. Alex Krizhevsky
8. Ruibo Liu
9. Diederik P. Kingma
10. Alec Radford

Who trusts Guangxuan Xu overall

1. Ruibo Liu
2. Freda Shi

Papers from Guangxuan Xu's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Attention is All you Need (Neural Information Processing Systems)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. ImageNet: A large-scale hierarchical image database
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
• On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmark (2021) — 117 citations
• Enhanced Offensive Language Detection Through Data Augmentation (2020) — 10 citations
• Mitigating Political Bias in Language Models Through Reinforced Calibration (2021) — 116 citations