Chia-Hua Ho

Artificial Intelligence · 2011–2012

A researcher in the Science corpus: 2 papers; trusts 97 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 59 citations into it; #7,260 of 35,345 by trust here, #15,236 by citations
• Optimization & Training — 40 citations into it
• Computer Vision and Pattern Recognition — 38 citations into it; #21,440 of 21,575 by trust here, #14,478 by citations
• Computational Mechanics — 29 citations into it; #9,896 of 10,001 by trust here, #5,157 by citations
• Statistics and Probability — 18 citations into it; #2,595 of 12,932 by trust here, #5,857 by citations
• Statistical Learning Theory — 12 citations into it
• Mathematical Optimization — 11 citations into it
• Management Science and Operations Research — 7 citations into it

Whom Chia-Hua Ho trusts overall

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

1. Chih-Jen Lin
2. Thorsten Joachims
3. Shai Shalev-Shwartz
4. N. Schraudolph
5. L. Eon Bottou
6. Robert Tibshirani
7. Jerome H. Friedman
8. Leo Breiman
9. Yuhai Wu
10. Yoram Singer

Who trusts Chia-Hua Ho overall

1. Ilya Trofimov
2. Virginia Smith
3. Kimon Fountoulakis
4. A. Genkin
5. Simone Forte
6. Dr. Matthias Seeger
7. Weiwei Liu
8. Yashoteja Prabhu
9. Tyler B. Johnson
10. Jason D. Lee

Papers from Chia-Hua Ho's perspective

Ordered by how strongly the researchers Chia-Hua Ho 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• An improved GLMNET for l1-regularized logistic regression (2011) — 244 citations
• Recent Advances of Large-Scale Linear Classification (2012) — 306 citations