Taeho C. Jo

Artificial Intelligence · 2004–2019

A researcher in the Science corpus: 5 papers; trusts 71 researchers and is trusted by 51 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 35 citations into it; #4,128 of 35,345 by trust here, #8,525 by citations
• Computer Vision and Pattern Recognition — 26 citations into it
• Molecular Biology — 24 citations into it; #9,149 of 17,773 by trust here, #15,203 by citations
• Computational Biology & Bioinformatics — 17 citations into it
• Materials Chemistry — 13 citations into it
• Model Architectures — 13 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Probabilistic & Bayesian Methods — 12 citations into it

Whom Taeho C. Jo trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. J. Urgen Schmidhuber
4. Alex Krizhevsky
5. Ilya Sutskever
6. L. Eon Bottou
7. Yann LeCun
8. Robert E. Schapire
9. Paul J. Werbos
10. Jianlin Cheng

Who trusts Taeho C. Jo overall

1. Alberto Fernández
2. Ajinkya More
3. Qiong Wei
4. Paula Branco
5. Victoria López
6. Jie Hou
7. Rukshan Batuwita
8. Nitesh V. Chawla
9. Felix Last
10. Salvador Garcia

Papers from Taeho C. Jo's perspective

Ordered by how strongly the researchers Taeho C. Jo 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. ImageNet Large Scale Visual Recognition Challenge
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Improving Protein Fold Recognition by Deep Learning Networks (2015) — 114 citations
• Class imbalances versus small disjuncts (2004) — 747 citations
• A Multiple Resampling Method for Learning from Imbalanced Data Sets (2004) — 1,076 citations
• Improving protein fold recognition by random forest (2014) — 54 citations
• Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data (2019) — 618 citations