Zheng Zhao

Artificial Intelligence · International Conference on Machine Learning · 2007–2013

A researcher in the Science corpus: 6 papers; trusts 81 researchers and is trusted by 46 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 44 citations into it; #4,570 of 35,345 by trust here, #11,072 by citations
• Computer Vision and Pattern Recognition — 20 citations into it; #3,793 of 21,575 by trust here, #6,485 by citations
• Computational Theory and Mathematics — 13 citations into it
• Evaluation & Benchmarks — 13 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Statistical Learning Theory — 12 citations into it
• Information Systems — 11 citations into it
• Statistics and Probability — 8 citations into it

Whom Zheng Zhao trusts overall

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

1. Huan Liu
2. Eric R. Ziegel
3. Igor Kononenko
4. Xiaofei He
5. Isabelle M Guyon
6. Lei Yu
7. Jianbo Shi
8. Marko Robnik-Sikonja
9. Andre´ Elisseeff
10. Jennifer G. Dy

Who trusts Zheng Zhao overall

1. Feiping Nie
2. Verónica Bolón-Canedo
3. Zechao Li
4. Huan Liu
5. Jundong Li
6. Saúl Solorio-Fernández
7. Jiliang Tang
8. Noelia Sánchez-Maroño
9. Chenping Hou
10. Andreas Henelius

Papers from Zheng Zhao's perspective

Ordered by how strongly the researchers Zheng 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Spectral feature selection for supervised and unsupervised learning (2007) — 983 citations
• On Similarity Preserving Feature Selection (2013) — 316 citations
• Semi-supervised Feature Selection via Spectral Analysis (2007) — 305 citations
• Efficient Spectral Feature Selection with Minimum Redundancy (2010) — 296 citations
• Searching for interacting features in subset selection (2009) — 115 citations
• Searching for Interacting Features (2007) — 274 citations