Fangxiang Feng

Artificial Intelligence · 2013–2021

A researcher in the Science corpus: 4 papers; trusts 82 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 43 citations into it
• Computer Vision and Pattern Recognition — 34 citations into it; #8,434 of 21,575 by trust here, #6,737 by citations
• Representation & Self-Supervised Learning — 20 citations into it
• Probabilistic & Bayesian Methods — 14 citations into it
• Natural Language Processing — 11 citations into it
• Model Architectures — 7 citations into it
• Multimodal Learning — 6 citations into it; #460 of 1,211 by trust here, #770 by citations

Whom Fangxiang Feng trusts overall

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

1. Geoffrey E. Hinton
2. Ruslan Salakhutdinov
3. Yoshua Bengio
4. Max Welling
5. Yann LeCun
6. Nitish Srivastava
7. Alex Krizhevsky
8. Diederik P. Kingma
9. Radford M. Neal
10. Michael I. Jordan

Who trusts Fangxiang Feng overall

1. Yuxin Peng
2. Shiv Ram Dubey
3. Mingsheng Long
4. Xing Xu
5. Miaomiao Cheng
6. Liangli Zhen
7. Jianmin Wang
8. Kaiye Wang
9. Tingting Qiao
10. Xavier Favory

Papers from Fangxiang Feng's perspective

Ordered by how strongly the researchers Fangxiang 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Challenges in representation learning: A report on three machine learning contests (2013) — 2,020 citations
• Cross-modal Retrieval with Correspondence Autoencoder (2014) — 585 citations
• Dual Graph Convolutional Networks for Aspect-based Sentiment Analysis (2021) — 454 citations
• Constructing Hierarchical Image-tags Bimodal Representations for Word Tags Alternative Choice (2013) — 12 citations