Xiaocheng Feng

Artificial Intelligence · 2015–2023

A researcher in the Science corpus: 7 papers; trusts 106 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 250 citations into it; #16,061 of 35,345 by trust here, #3,797 by citations
• Natural Language Processing — 177 citations into it; #4,522 of 5,688 by trust here, #1,274 by citations
• Computer Vision and Pattern Recognition — 62 citations into it
• Evaluation & Benchmarks — 48 citations into it
• Model Architectures — 36 citations into it
• Representation & Self-Supervised Learning — 20 citations into it
• Optimization & Training — 5 citations into it

Whom Xiaocheng Feng trusts overall

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

1. Yoshua Bengio
2. Dzmitry Bahdanau
3. Kyunghyun Cho
4. Ilya Sutskever
5. Geoffrey E. Hinton
6. Matthew D. Zeiler
7. Richard Socher
8. Diederik P. Kingma
9. Alex Graves
10. Alex Krizhevsky

Who trusts Xiaocheng Feng overall

1. Sijia Wang
2. Thien Huu Nguyen
3. Jian Liu
4. Xiao Liu
5. Pengfei Yu
6. Binxuan Huang
7. Duyu Tang
8. Mo Yu
9. Duo Chai
10. Ralph Grishman

Papers from Xiaocheng Feng's perspective

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

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

Their papers

• CodeBERT: A Pre-Trained Model for Programming and Natural Languages (2020) — 4,078 citations
• The Factual Inconsistency Problem in Abstractive Text Summarization: A Survey (2021) — 128 citations
• A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions (2023) — 3,329 citations
• Topic-to-Essay Generation with Neural Networks (2018) — 72 citations
• A language-independent neural network for event detection (2016) — 283 citations
• Effective LSTMs for Target-Dependent Sentiment Classification (2015) — 1,000 citations
• Target-Dependent Sentiment Classification with Long Short Term Memory (2015) — 123 citations