Shuo Wang
Artificial Intelligence · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · 2009–2013
A researcher in the Science corpus: 3 papers; trusts 38 researchers and is trusted by 31 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 22 citations into it; #6,283 of 35,345 by trust here, #13,962 by citations
- Information Systems — 12 citations into it; #2,260 of 8,771 by trust here, #6,195 by citations
- Electrical and Electronic Engineering — 7 citations into it; #1,745 of 16,539 by trust here, #8,038 by citations
- Statistical Learning Theory — 7 citations into it
- Software — 7 citations into it; #305 of 1,227 by trust here, #839 by citations
- Evaluation & Benchmarks — 5 citations into it
- Optimization & Training — 5 citations into it
Whom Shuo Wang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Nitesh V. Chawla
- Andrew P. Bradley
- Robert E. Schapire
- Steven L. Salzberg
- J. Ross Quinlan
- Yoav Freund
- Zhi-Hua Zhou
- Yanmin Sun
- Trevor J. Hastie
- Jaka Demsar
Who trusts Shuo Wang overall
- Harikrishna Narasimhan
- Chakkrit Kla Tantithamthavorn
- Alberto Fernández
- Mikel Galar
- Paula Branco
- Harsurinder Kaur
- Aida Ali
- Min-Ling Zhang
- Ming Tan
- Ahmed E. Hassan
Papers from Shuo Wang's perspective
Ordered by how strongly the researchers Shuo Wang trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- A decision-theoretic generalization of on-line learning and an application to boosting
- Experiments with a New Boosting Algorithm
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- SMOTE: Synthetic Minority Over-sampling Technique
- Going deeper with convolutions
Their papers
- Multiclass Imbalance Problems: Analysis and Potential Solutions (2012) — 553 citations
- Diversity analysis on imbalanced data sets by using ensemble models (2009) — 544 citations
- Using Class Imbalance Learning for Software Defect Prediction (2013) — 532 citations