Young-Bum Kim

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 4 papers; trusts 91 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 56 citations into it; #13,152 of 35,345 by trust here, #19,439 by citations
• Natural Language Processing — 22 citations into it
• Computer Vision and Pattern Recognition — 16 citations into it; #17,184 of 21,575 by trust here, #19,766 by citations
• Model Architectures — 16 citations into it
• Optimization & Training — 10 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Signal Processing — 5 citations into it

Whom Young-Bum Kim trusts overall

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

1. Diederik P. Kingma
2. Gökhan Tür
3. Sepp Hochreiter
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. J. Urgen Schmidhuber
7. Alex Graves
8. Jimmy Ba
9. Alex Krizhevsky
10. Ilya Sutskever

Who trusts Young-Bum Kim overall

1. Avik Ray
2. Sebastian Ruder
3. Xiaochuang Han
4. Nikhita Vedula
5. Yilin Shen
6. Dong-hyun Ham
7. Hwaran Lee
8. Barbara Plank
9. Joo-Kyung Kim
10. Hongxia Jin

Papers from Young-Bum Kim's perspective

Ordered by how strongly the researchers Young-Bum Kim 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. 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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Adversarial Adaptation of Synthetic or Stale Data (2017) — 50 citations
• Joint Learning of Domain Classification and Out-of-Domain Detection with Dynamic Class Weighting for Satisficing False Acceptance Rates (2018) — 53 citations
• Efficient Large-Scale Domain Classification with Personalized Attention (2018) — 20 citations
• ONENET: Joint domain, intent, slot prediction for spoken language understanding (2017) — 75 citations