Jin-Hwa Kim

Artificial Intelligence · with Zhang · 2015–2020

A researcher in the Science corpus: 5 papers; trusts 429 researchers and is trusted by 552 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 88 citations into it; #1,988 of 35,345 by trust here, #2,131 by citations
• Computer Vision and Pattern Recognition — 86 citations into it; #1,834 of 21,575 by trust here, #1,562 by citations
• Multimodal Learning — 40 citations into it
• Natural Language Processing — 27 citations into it
• Model Architectures — 25 citations into it; #301 of 2,278 by trust here, #571 by citations
• Optimization & Training — 21 citations into it
• Representation & Self-Supervised Learning — 10 citations into it; #744 of 3,145 by trust here, #518 by citations
• Graph Learning — 9 citations into it

Whom Jin-Hwa Kim trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Yoshua Bengio
4. Diederik P. Kingma
5. Alex Krizhevsky
6. Ilya Sutskever
7. Yann LeCun
8. Ruslan Salakhutdinov
9. Ross B. Girshick
10. Shaoqing Ren

Who trusts Jin-Hwa Kim overall

1. Kushal Kafle
2. Pan Lu
3. Rahaf Aljundi
4. Linjie Li
5. Zhe Gan
6. Sedigheh Eslami
7. Liunian Harold Li
8. Christopher Kanan
9. Junnan Li
10. Robik Shrestha

Papers from Jin-Hwa Kim's perspective

Ordered by how strongly the researchers Jin-Hwa 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Bilinear Attention Networks (2018) — 1,059 citations
• Overcoming Catastrophic Forgetting by Incremental Moment Matching (2017) — 738 citations
• Multimodal Residual Learning for Visual QA (2016) — 319 citations
• rnn : Recurrent Library for Torch (2015) — 43 citations
• DialGraph: Sparse Graph Learning Networks for Visual Dialog (2020) — 4 citations