Xiaochuang Han
Artificial Intelligence · 2019–2023
A researcher in the Science corpus: 6 papers; trusts 265 researchers and is trusted by 18 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 149 citations into it; #8,561 of 35,345 by trust here, #13,163 by citations
- Natural Language Processing — 97 citations into it
- Safety, Robustness & Interpretability — 47 citations into it; #947 of 2,251 by trust here, #1,731 by citations
- Large Language Models — 31 citations into it
- Computer Vision and Pattern Recognition — 29 citations into it
- Evaluation & Benchmarks — 27 citations into it
- Representation & Self-Supervised Learning — 15 citations into it
- Prompting & Reasoning — 13 citations into it
Whom Xiaochuang Han trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Jacob Devlin
- Geoffrey E. Hinton
- Yoshua Bengio
- Karen Simonyan
- Diederik P. Kingma
- Ilya Sutskever
- Alex Krizhevsky
- Yann LeCun
- Kaiming He
- Richard Socher
Who trusts Xiaochuang Han overall
- Eyal Ben-David
- Dheeraj Rajagopal
- Pouya Pezeshkpour
- Nadav Oved
- Guokun Lai
- Vidhisha Balachandran
- Suchin Gururangan
- Sarthak Jain
- Ekin Akyürek
- Roi Reichart
Papers from Xiaochuang Han's perspective
Ordered by how strongly the researchers Xiaochuang Han trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- 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
- Gradient-based learning applied to document recognition
- 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)
Their papers
- Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions (2020) — 210 citations
- Influence Tuning: Demoting Spurious Correlations via Instance Attribution and Instance-Driven Updates (2021) — 38 citations
- Unsupervised Domain Adaptation of Contextualized Embeddings for Sequence Labeling (2019) — 185 citations
- Trusting Your Evidence: Hallucinate Less with Context-aware Decoding (2023) — 387 citations
- Can Language Models Solve Graph Problems in Natural Language? (2023) — 380 citations
- Unsupervised Domain Adaptation of Contextualized Embeddings: A Case Study in Early Modern English (2019) — 20 citations