Fuchun Peng

Artificial Intelligence · 2004–2019

A researcher in the Science corpus: 6 papers; trusts 28 researchers and is trusted by 39 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 83 citations into it; #5,869 of 35,345 by trust here, #11,921 by citations
• Natural Language Processing — 69 citations into it; #3,251 of 5,688 by trust here, #3,687 by citations
• Information Systems — 6 citations into it; #2,824 of 8,771 by trust here, #5,942 by citations
• Management Science and Operations Research — 5 citations into it

Whom Fuchun Peng trusts overall

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

1. Robert Malouf
2. Andrew McCallum
3. Fei Sha
4. Alex Graves
5. Fernando C Pereira
6. Geoffrey E. Hinton
7. Sepp Hochreiter
8. J. Urgen Schmidhuber
9. John D. Lafferty
10. Yoshua Bengio

Who trusts Fuchun Peng overall

1. Charles Sutton
2. Taku Kudo
3. Andrew McCallum
4. Patrice Lopez
5. Percy Liang
6. Fei Wu
7. Raymond J. Mooney
8. Niklas Jakob
9. Faezeh Ensan
10. Pi-Chuan Chang

Papers from Fuchun Peng's perspective

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

1. Long Short-Term Memory
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Attention is All you Need (Neural Information Processing Systems)
4. ImageNet classification with deep convolutional neural networks
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Learning Multiple Layers of Features from Tiny Images

Their papers

• Chinese Segmentation and New Word Detection using Conditional Random Fields (2004) — 520 citations
• Grapheme-to-phoneme conversion using Long Short-Term Memory recurrent neural networks (2015) — 202 citations
• Mix-review: Alleviate Forgetting in the Pretrain-Finetune Framework for Neural Language Generation Models (2019) — 9 citations
• Information extraction from research papers using conditional random fields (2006) — 267 citations
• Analyzing the Forgetting Problem in the Pretrain-Finetuning of Dialogue Response Models (2019) — 16 citations
• Accurate Information Extraction from Research Papers using Conditional Random Fields (2004) — 338 citations