Xiangyu Zhao

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 5 papers; trusts 178 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 90 citations into it
• Computer Vision and Pattern Recognition — 74 citations into it
• Information Systems — 61 citations into it; #5,536 of 8,771 by trust here, #2,667 by citations
• Reinforcement Learning — 26 citations into it
• Model Architectures — 22 citations into it
• Management Science and Operations Research — 19 citations into it; #7,799 of 11,946 by trust here, #3,357 by citations
• Recommender Systems — 17 citations into it
• Representation & Self-Supervised Learning — 12 citations into it

Whom Xiangyu Zhao trusts overall

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

1. Longxin Lin
2. Richard S. Sutton
3. Geoffrey E. Hinton
4. Andrew Y. Ng
5. Alex Krizhevsky
6. Yoshua Bengio
7. Kaiming He
8. Yann LeCun
9. Ilya Sutskever
10. Diederik P. Kingma

Who trusts Xiangyu Zhao overall

1. M. M. Afsar
2. Shoujin Wang
3. Trafford Crump
4. Jiawei Chen
5. Shuai Zhang
6. Behrouz Homayoun Far
7. L. Zhang
8. Christopher Fifty
9. Longbing Cao
10. Long Xia

Papers from Xiangyu Zhao's perspective

Ordered by how strongly the researchers Xiangyu Zhao 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. 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. Going deeper with convolutions

Their papers

• A Modulation Module for Multi-task Learning with Applications in Image Retrieval (2018) — 155 citations
• Deep reinforcement learning for page-wise recommendations (2018) — 442 citations
• Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning (2018) — 386 citations
• Deep Reinforcement Learning for List-wise Recommendations (2017) — 195 citations
• AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations (2020) — 135 citations