Ruoxi Wang

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 2 papers; trusts 11 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 35 citations into it; #25,250 of 35,345 by trust here, #14,181 by citations
• Information Systems — 24 citations into it
• Computer Vision and Pattern Recognition — 20 citations into it; #19,221 of 21,575 by trust here, #10,154 by citations
• Management Science and Operations Research — 12 citations into it
• Model Architectures — 11 citations into it
• Optimization & Training — 6 citations into it
• Recommender Systems — 6 citations into it

Whom Ruoxi Wang trusts overall

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

1. Diederik P. Kingma
2. Steffen Rendle
3. Kaiming He
4. Sergey Ioffe
5. Jimmy Ba
6. Geoffrey E. Hinton
7. Gene H. Golub
8. Christian Szegedy
9. Long Chen
10. Alex Krizhevsky

Who trusts Ruoxi Wang overall

1. Yu. V. Gorishniy
2. Justin Engelmann
3. Tongwen Huang
4. Bin Liu
5. Ivan Rubachev
6. Le Wu
7. Pengyu Zhao
8. Zilong Zhao
9. Hongwei Wang
10. Qingquan Song

Papers from Ruoxi Wang's perspective

Ordered by how strongly the researchers Ruoxi Wang 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Deep & Cross Network for Ad Click Predictions (2017) — 1,535 citations
• DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems (2020) — 865 citations