Jeonghee Kim

Artificial Intelligence · 2016–2017

A researcher in the Science corpus: 4 papers; trusts 452 researchers and is trusted by 42 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 81 citations into it; #6,075 of 35,345 by trust here, #5,281 by citations
• Computer Vision and Pattern Recognition — 79 citations into it; #4,582 of 21,575 by trust here, #4,001 by citations
• Multimodal Learning — 30 citations into it; #244 of 1,211 by trust here, #499 by citations
• Natural Language Processing — 29 citations into it
• Model Architectures — 26 citations into it
• Optimization & Training — 17 citations into it
• Question Answering & Reading Comprehension — 5 citations into it

Whom Jeonghee Kim trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Yoshua Bengio
4. Alex Krizhevsky
5. Karen Simonyan
6. Ilya Sutskever
7. Diederik P. Kingma
8. Yann LeCun
9. Alex Graves
10. Andrej Karpathy

Who trusts Jeonghee Kim overall

1. Kurt Shuster
2. Zhou Yu
3. Kushal Kafle
4. Niluthpol Chowdhury Mithun
5. Fartash Faghri
6. Ying Zhang
7. Dalu Guo
8. Jun Yu
9. Robik Shrestha

Papers from Jeonghee Kim's perspective

Ordered by how strongly the researchers Jeonghee Kim trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Multimodal Residual Learning for Visual QA (2016) — 319 citations
• Dual Attention Networks for Multimodal Reasoning and Matching (2016) — 716 citations
• Dual-memory neural networks for modeling cognitive activities of humans via wearable sensors (2017) — 9 citations
• Dual-Memory Deep Learning Architectures for Lifelong Learning of Everyday Human Behaviors (2016) — 44 citations