Kun Xiong

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 5 papers; trusts 35 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 86 citations into it; #15,222 of 35,345 by trust here, #7,172 by citations
• Natural Language Processing — 82 citations into it
• Computer Vision and Pattern Recognition — 50 citations into it; #11,460 of 21,575 by trust here, #5,987 by citations
• Information Retrieval & Search — 45 citations into it; #502 of 1,022 by trust here, #228 by citations
• Large Language Models — 18 citations into it
• Information Systems — 16 citations into it
• General Social Sciences — 6 citations into it

Whom Kun Xiong trusts overall

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

1. Alec Radford
2. Jacob Devlin
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Peilin Yang
6. Diederik P. Kingma
7. Richard Socher
8. Wei Yang
9. Ilya Sutskever
10. Alex Krizhevsky

Who trusts Kun Xiong overall

1. Wei Yang
2. Zeynep Akkalyoncu Yilmaz
3. Wenhan Xiong

Papers from Kun Xiong's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
3. Long Short-Term Memory
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. ImageNet classification with deep convolutional neural networks
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. ImageNet: A large-scale hierarchical image database
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Gradient-based learning applied to document recognition
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• End-to-End Open-Domain Question Answering with BERTserini (2019) — 526 citations
• Data Augmentation for BERT Fine-Tuning in Open-Domain Question Answering (2019) — 70 citations
• Segatron: Segment-Aware Transformer for Language Modeling and Understanding (2020) — 25 citations
• Distant Supervision for Multi-Stage Fine-Tuning in Retrieval-Based Question Answering (2020) — 23 citations
• Approximate Nearest Neighbor Search and Lightweight Dense Vector Reranking in Multi-Stage Retrieval Architectures (2020) — 17 citations