Ruby Chiu-Hsing Weng

Artificial Intelligence · 2003–2008

A researcher in the Science corpus: 4 papers; trusts 13 researchers and is trusted by 55 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 12 citations into it; #11,681 of 35,345 by trust here, #13,962 by citations
• Optimization & Training — 10 citations into it; #897 of 2,601 by trust here, #1,468 by citations
• Statistical Learning Theory — 9 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it; #7,857 of 21,575 by trust here, #9,563 by citations

Whom Ruby Chiu-Hsing Weng trusts overall

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

1. J. Platt
2. Thorsten Joachims
3. Trond Steihaug
4. John C. Platt
5. Trevor J. Hastie
6. Bernhard E. Boser
7. Yann LeCun
8. David D. Lewis
9. Robert Tibshirani
10. Vladimir Naumovich Vapnik

Who trusts Ruby Chiu-Hsing Weng overall

1. L. Eon Bottou
2. Chunyuan Li
3. Chih-Jen Lin
4. Hsiang-Fu Yu
5. Paulo Cortez
6. Cho-Jui Hsieh
7. Dhruv Kumar Mahajan
8. Kai-Wei Chang
9. Fabian Pedregosa
10. Jia Deng

Papers from Ruby Chiu-Hsing Weng's perspective

Ordered by how strongly the researchers Ruby Chiu-Hsing Weng trusts endorse each paper. Same corpus, a different order for every researcher.

1. Gradient-based learning applied to document recognition
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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

• Trust region Newton methods for large-scale logistic regression (2007) — 333 citations
• A note on Platt’s probabilistic outputs for support vector machines (2007) — 1,003 citations
• Trust Region Newton Method for Logistic Regression (2008) — 347 citations
• Probability Estimates for Multi-class Classification by Pairwise Coupling (2003) — 2,028 citations