Hwanjo Yu

Artificial Intelligence · 2002–2019

A researcher in the Science corpus: 15 papers; trusts 117 researchers and is trusted by 45 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 154 citations into it; #5,123 of 35,345 by trust here, #6,157 by citations
• Computer Vision and Pattern Recognition — 31 citations into it; #7,044 of 21,575 by trust here, #12,992 by citations
• Information Systems — 29 citations into it; #2,158 of 8,771 by trust here, #2,539 by citations
• Natural Language Processing — 27 citations into it
• Optimization & Training — 19 citations into it
• Representation & Self-Supervised Learning — 18 citations into it
• Computational Mathematics — 17 citations into it; #610 of 612 by trust here, #372 by citations
• Management Science and Operations Research — 17 citations into it

Whom Hwanjo Yu trusts overall

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

1. Thorsten Joachims
2. Jaideep Vaidya
3. glenn. fung
4. Vladimir Naumovich Vapnik
5. C. Burges
6. Olvi L. Mangasarian
7. Chris Clifton
8. Corinna Cortes
9. Yuhai Wu
10. Yiming Yang

Who trusts Hwanjo Yu overall

1. Payman Mohassel
2. Shehroz S. Khan
3. Jessa Bekker
4. Qinbin Li
5. B. Liu
6. Wee Sun Lee
7. Charu C. Aggarwal
8. Michael G. Madden
9. Jesse Davis
10. Charles Peter Elkan

Papers from Hwanjo Yu's perspective

Ordered by how strongly the researchers Hwanjo Yu 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
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

• Classifying large data sets using SVMs with hierarchical clusters (2003) — 353 citations
• Convolutional Matrix Factorization for Document Context-Aware Recommendation (2016) — 769 citations
• Privacy-preserving SVM classification (2008) — 194 citations
• PEBL: Web page classification without negative examples (2004) — 301 citations
• PEBL: positive example based learning for Web page classification using SVM (2002) — 333 citations
• Nonlinear Support Vector Machine Visualization for Risk Factor Analysis Using Nomograms and Localized Radial Basis Function Kernels (2008) — 71 citations
• Discriminative and Distinct Phenotyping by Constrained Tensor Factorization (2017) — 40 citations
• Unsupervised Attributed Multiplex Network Embedding (2019) — 341 citations
• L G ] 1 1 A pr 2 01 7 Federated Tensor Factorization for Computational Phenotyping (2018)
• Out-of-domain Detection based on Generative Adversarial Network (2018) — 70 citations
• Neural sentence embedding using only in-domain sentences for out-of-domain sentence detection in dialog systems (2017) — 56 citations
• Federated Tensor Factorization for Computational Phenotyping (2017) — 129 citations
• Fast and Robust Parallel SGD Matrix Factorization (2015) — 53 citations
• Privacy-preserving SVM using nonlinear kernels on horizontally partitioned data (2006) — 225 citations
• Privacy-Preserving SVM Classification on Vertically Partitioned Data (2006) — 232 citations