Xiaopeng Li

Artificial Intelligence · with She · 2017

A researcher in the Science corpus: 1 paper; trusts 50 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 12 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it; #7,579 of 21,575 by trust here, #14,151 by citations
• Recommender Systems — 9 citations into it
• Information Systems — 7 citations into it; #1,925 of 8,771 by trust here, #4,518 by citations
• Natural Language Processing — 6 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it
• Signal Processing — 5 citations into it; #2,042 of 7,572 by trust here, #3,913 by citations

Whom Xiaopeng Li trusts overall

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

1. Ruslan Salakhutdinov
2. Diederik P. Kingma
3. Michael J. Pazzani
4. Geoffrey E. Hinton
5. Ken Lang
6. Yoshua Bengio
7. Andriy Mnih
8. Alex Krizhevsky
9. Max Welling
10. David M. Blei

Who trusts Xiaopeng Li overall

1. Hao Wang
2. Aminu Da’u
3. Maurizio Ferrari Dacrema
4. Zeynep Batmaz
5. Dit-Yan Yeung
6. Jianxin Ma
7. Yi Tay
8. Naomie Binti Salim
9. Paolo Cremonesi
10. Xiang Wang

Papers from Xiaopeng Li's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Collaborative Variational Autoencoder for Recommender Systems (2017) — 458 citations