Daniel S. Yeung

Artificial Intelligence · 2005–2016

A researcher in the Science corpus: 7 papers; trusts 24 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 68 citations into it; #27,456 of 35,345 by trust here, #14,718 by citations
• Safety, Robustness & Interpretability — 39 citations into it; #1,686 of 2,251 by trust here, #1,481 by citations
• Computer Networks and Communications — 34 citations into it; #7,027 of 9,886 by trust here, #3,523 by citations
• Signal Processing — 29 citations into it; #5,492 of 7,572 by trust here, #2,457 by citations
• Information Systems — 22 citations into it
• Privacy & Security — 14 citations into it
• Representation & Self-Supervised Learning — 11 citations into it
• Computational Theory and Mathematics — 8 citations into it; #7,199 of 12,182 by trust here, #2,060 by citations

Whom Daniel S. Yeung trusts overall

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

1. Battista Biggio
2. Nitesh V. Chawla
3. Yoshua Bengio
4. Fabrizio Sebastiani
5. Giorgio Fumera
6. Etienne E. Kerre
7. Donald Michie
8. Vladimir Naumovich Vapni
9. Isabelle M Guyon
10. Richard Socher

Who trusts Daniel S. Yeung overall

1. William Zhu
2. Junyi Chai
3. Richard Jensen
4. Qinghua Hu
5. James Nga-Kwok Liu
6. Battista Biggio
7. K. V. Babitha

Papers from Daniel S. Yeung's perspective

Ordered by how strongly the researchers Daniel S. Yeung 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• The parameterization reduction of soft sets and its applications (2005) — 736 citations
• On the generalization of fuzzy rough sets (2005) — 436 citations
• Attributes Reduction Using Fuzzy Rough Sets (2008) — 313 citations
• Dual autoencoders features for imbalance classification problem (2016) — 101 citations
• Adversarial Feature Selection Against Evasion Attacks (2016) — 240 citations
• One-and-a-Half-Class Multiple Classifier Systems for Secure Learning Against Evasion Attacks at Test Time (2015) — 51 citations
• Sensitivity analysis applied to the construction of radial basis function networks (2005) — 66 citations