Baotian Hu

Natural Language Processing · 2014–2020

A researcher in the Science corpus: 8 papers; trusts 328 researchers and is trusted by 110 researchers here, as inferred from the citation graph.

Subfields

• Natural Language Processing — 98 citations into it; #641 of 5,688 by trust here, #951 by citations
• Artificial Intelligence — 94 citations into it; #2,300 of 35,345 by trust here, #3,878 by citations
• Computer Vision and Pattern Recognition — 62 citations into it; #3,585 of 21,575 by trust here, #3,713 by citations
• Generative Models — 23 citations into it
• Model Architectures — 22 citations into it
• Representation & Self-Supervised Learning — 17 citations into it
• Optimization & Training — 13 citations into it
• Evaluation & Benchmarks — 11 citations into it; #1,536 of 3,147 by trust here, #2,166 by citations

Whom Baotian Hu trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Tomas Mikolov
4. Chin-Yew Lin
5. Ilya Sutskever
6. Richard Socher
7. Matthew D. Zeiler
8. Alex Krizhevsky
9. Diederik P. Kingma
10. Yann LeCun

Who trusts Baotian Hu overall

1. Zhengdong Lu
2. Jiafeng Guo
3. Wenpeng Yin
4. Hang Li
5. Ayana
6. C. D. Santos
7. Xipeng Qiu
8. Dinghan Shen
9. Ming Tan
10. Tom Young

Papers from Baotian Hu's perspective

Ordered by how strongly the researchers Baotian Hu 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Convolutional Neural Network Architectures for Matching Natural Language Sentences (2014) — 1,380 citations
• Learning to Extract Coherent Summary via Deep Reinforcement Learning (2018) — 180 citations
• LCSTS: A Large Scale Chinese Short Text Summarization Dataset (2015) — 368 citations
• Text-Guided Neural Image Inpainting (2020) — 67 citations
• Context-Dependent Translation Selection Using Convolutional Neural Network (2015) — 27 citations
• A novel word embedding learning model using the dissociation between nouns and verbs (2016) — 26 citations
• ICRC-HIT: A Deep Learning based Comment Sequence Labeling System for Answer Selection Challenge (2015) — 23 citations
• A Short Texts Matching Method Using Shallow Features and Deep Features (2014) — 15 citations