Xinxi Wang

Artificial Intelligence · 2013–2014

A researcher in the Science corpus: 2 papers; trusts 24 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 15 citations into it
• Information Systems — 14 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it; #6,270 of 21,575 by trust here, #11,498 by citations
• Recommender Systems — 9 citations into it; #70 of 233 by trust here, #151 by citations
• Signal Processing — 8 citations into it; #1,500 of 7,572 by trust here, #2,457 by citations
• Probabilistic & Bayesian Methods — 6 citations into it
• Reinforcement Learning — 6 citations into it

Whom Xinxi Wang trusts overall

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

1. Geoffrey E. Hinton
2. Ruslan Salakhutdinov
3. Yoshua Bengio
4. Stephen W. Carden
5. Honglak Lee
6. Yann LeCun
7. Feng Niu
8. Deepak K. Agarwal
9. Yehuda Koren
10. Philippe Hamel

Who trusts Xinxi Wang overall

1. Hao Wang
2. Aminu Da’u
3. Dawen Liang
4. Sheng Li
5. Trapit Bansal
6. Xiangnan He
7. Yongfeng Zhang
8. Carl Yang
9. Xuemeng Song
10. Dit-Yan Yeung

Papers from Xinxi Wang's perspective

Ordered by how strongly the researchers Xinxi Wang 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Improving Content-based and Hybrid Music Recommendation using Deep Learning (2014) — 373 citations
• Exploration in Interactive Personalized Music Recommendation: A Reinforcement Learning Approach (2013) — 120 citations