Heung-Il Suk

Computer Vision and Pattern Recognition · 2013–2017

A researcher in the Science corpus: 9 papers; trusts 424 researchers and is trusted by 57 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 120 citations into it; #3,775 of 21,575 by trust here, #2,562 by citations
• Artificial Intelligence — 77 citations into it; #7,281 of 35,345 by trust here, #5,970 by citations
• Cognitive Neuroscience — 55 citations into it; #9,524 of 10,852 by trust here, #8,086 by citations
• Radiology, Nuclear Medicine and Imaging — 52 citations into it; #2,242 of 3,265 by trust here, #597 by citations
• Psychiatry and Mental health — 50 citations into it; #77 of 1,338 by trust here, #249 by citations
• Neurology — 28 citations into it; #78 of 1,455 by trust here, #185 by citations
• Physiology — 26 citations into it
• Model Architectures — 25 citations into it

Whom Heung-Il Suk trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Ruslan Salakhutdinov
4. Hugo Larochelle
5. Christos Davatzikos
6. David G. Lowe
7. Alex Krizhevsky
8. Nitish Srivastava
9. Robert Tibshirani
10. Daoqiang Zhang

Who trusts Heung-Il Suk overall

1. Dinggang Shen
2. Jyoti Islam
3. Mufti Mahmud
4. Ehsan Hosseini-Asl
5. Jun Shi
6. Yanqing Zhang
7. Guorong Wu
8. John Arevalo
9. Seong-Whan Lee
10. Fuyong Xing

Papers from Heung-Il Suk's perspective

Ordered by how strongly the researchers Heung-Il Suk 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Deep Learning Based Imaging Data Completion for Improved Brain Disease Diagnosis (2014) — 470 citations
• View-Invariant 3D Action Recognition Using Spatiotemporal Self-Similarities from Depth Camera (2014) — 9 citations
• Deep ensemble learning of sparse regression models for brain disease diagnosis (2017) — 307 citations
• Deep Learning in Medical Image Analysis (2017) — 2,935 citations
• State-space model with deep learning for functional dynamics estimation in resting-state fMRI (2016) — 298 citations
• Volumetric spatial feature representation for view-invariant human action recognition using a depth camera (2015) — 11 citations
• Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis (2014) — 913 citations
• Latent feature representation with stacked auto-encoder for AD/MCI diagnosis (2013) — 516 citations
• Deep Learning-Based Feature Representation for AD/MCI Classification (2013) — 457 citations