Ke Zhou

Information Systems · with Zha · 2009–2013

A researcher in the Science corpus: 6 papers; trusts 28 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Information Systems — 30 citations into it; #2,216 of 8,771 by trust here, #2,912 by citations
• Artificial Intelligence — 29 citations into it
• Management Science and Operations Research — 21 citations into it; #4,087 of 11,946 by trust here, #4,921 by citations
• Computer Vision and Pattern Recognition — 13 citations into it; #11,599 of 21,575 by trust here, #15,529 by citations
• Recommender Systems — 9 citations into it; #122 of 233 by trust here, #163 by citations
• Statistical and Nonlinear Physics — 8 citations into it
• Statistical Learning Theory — 6 citations into it
• Representation & Self-Supervised Learning — 6 citations into it; #2,536 of 3,145 by trust here, #2,957 by citations

Whom Ke Zhou trusts overall

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

1. Yehuda Koren
2. David R. Hunter
3. Thomas Hofmann
4. Arkadiusz Paterek
5. Grace Wahba
6. Al Mamunur Rashid
7. Jason D. M. Rennie
8. Nati Srebro
9. Jerome H. Friedman
10. Deepak K. Agarwal

Who trusts Ke Zhou overall

1. Nan Du
2. Yongfeng Zhang
3. Thibault Jaisson
4. Yiyi Tao
5. Mehdi Elahi
6. Xiaoxue Zhao
7. Hanwang Zhang
8. Yichen Wang
9. Simiao Zuo
10. Jiaxi Tang

Papers from Ke Zhou's perspective

Ordered by how strongly the researchers Ke Zhou 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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

• Functional matrix factorizations for cold-start recommendation (2011) — 292 citations
• Learning multiple-question decision trees for cold-start recommendation (2013) — 94 citations
• Learning Social Infectivity in Sparse Low-rank Networks Using Multi-dimensional Hawkes Processes (2013) — 430 citations
• Learning Triggering Kernels for Multi-dimensional Hawkes Processes (2013) — 278 citations
• Learning binary codes for collaborative filtering (2012) — 111 citations
• Multi-task learning for learning to rank in web search (2009) — 41 citations