Weiwei Cheng

Artificial Intelligence · Machine-mediated learning · 2008–2014

A researcher in the Science corpus: 9 papers; trusts 67 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 67 citations into it; #9,138 of 35,345 by trust here, #9,557 by citations
• Reinforcement Learning — 31 citations into it
• Information Systems — 22 citations into it
• Statistics and Probability — 19 citations into it
• Management Science and Operations Research — 15 citations into it; #3,848 of 11,946 by trust here, #4,602 by citations
• Molecular Biology — 14 citations into it
• Evaluation & Benchmarks — 12 citations into it
• Computer Vision and Pattern Recognition — 11 citations into it

Whom Weiwei Cheng trusts overall

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

1. Jaka Demsar
2. Grigorios Tsoumakas
3. Robert E. Schapire
4. Yufan Zhao
5. L. Eon Bottou
6. Fabrizio Sebastiani
7. Eyke Hüllermeier
8. Johannes Fürnkranz
9. Andre´ Elisseeff
10. Min-Ling Zhang

Who trusts Weiwei Cheng overall

1. Jesse Read
2. Francisco Charte
3. Krzysztof Dembczynski
4. Róbert Istvan Busa-Fekete
5. Min-Ling Zhang
6. Mohammad S. Sorower
7. Antonio Jesús Rivera
8. Eyke Hüllermeier
9. Bernhard Pfahringer
10. Eleftherios Spyromitros Xioufis

Papers from Weiwei Cheng's perspective

Ordered by how strongly the researchers Weiwei Cheng 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. 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

• Preference-based reinforcement learning: evolutionary direct policy search using a preference-based racing algorithm (2014) — 73 citations
• Preference-based reinforcement learning: a formal framework and a policy iteration algorithm (2012) — 177 citations
• Preference-Based Policy Iteration: Leveraging Preference Learning for Reinforcement Learning (2011) — 64 citations
• Label ranking by learning pairwise preferences (2008) — 589 citations
• On label dependence and loss minimization in multi-label classification (2012) — 330 citations
• Preference-based Evolutionary Direct Policy Search (2013) — 22 citations
• Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains (2010) — 506 citations
• Label Ranking Methods based on the Plackett-Luce Model (2010) — 112 citations
• Combining instance-based learning and logistic regression for multilabel classification (2009) — 470 citations