Jonghoon Jin

Computer Vision and Pattern Recognition · 2014–2015

A researcher in the Science corpus: 4 papers; trusts 270 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 52 citations into it; #7,694 of 21,575 by trust here, #6,426 by citations
• Artificial Intelligence — 32 citations into it; #11,836 of 35,345 by trust here, #13,163 by citations
• Model Architectures — 18 citations into it; #1,454 of 2,278 by trust here, #1,726 by citations
• Representation & Self-Supervised Learning — 17 citations into it
• Safety, Robustness & Interpretability — 9 citations into it; #1,003 of 2,251 by trust here, #1,945 by citations
• Optimization & Training — 8 citations into it
• ML Systems & Hardware — 7 citations into it
• Computer Networks and Communications — 5 citations into it

Whom Jonghoon Jin trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yann LeCun
4. Christian Szegedy
5. Yoshua Bengio
6. Karen Simonyan
7. Ilya Sutskever
8. Ronan Collobert
9. Ian J. Goodfellow
10. Rob Fergus

Who trusts Jonghoon Jin overall

1. Agnieszka Mikołajczyk
2. Sachin Mehta
3. Naveed Akhtar
4. Waseem Rawat
5. Jochen Gast
6. Shuxuan Guo
7. Xiaohan Ding
8. Shaohui Liu
9. Mikhail Figurnov
10. Liang-Chieh Chen

Papers from Jonghoon Jin's perspective

Ordered by how strongly the researchers Jonghoon Jin 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
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

• Flattened Convolutional Neural Networks for Feedforward Acceleration (2014) — 264 citations
• A 240 G-ops/s Mobile Coprocessor for Deep Neural Networks (2014) — 299 citations
• Robust Convolutional Neural Networks under Adversarial Noise (2015) — 80 citations
• Convolutional Clustering for Unsupervised Learning (2015) — 85 citations