Inho Kang
Artificial Intelligence · 2003–2021
A researcher in the Science corpus: 4 papers; trusts 119 researchers and is trusted by 7 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 88 citations into it; #16,193 of 35,345 by trust here, #11,921 by citations
- Computer Vision and Pattern Recognition — 58 citations into it; #19,047 of 21,575 by trust here, #12,992 by citations
- Natural Language Processing — 55 citations into it; #4,998 of 5,688 by trust here, #4,112 by citations
- Model Architectures — 9 citations into it
- Question Answering & Reading Comprehension — 9 citations into it
- Information Systems — 5 citations into it; #3,753 of 8,771 by trust here, #5,713 by citations
Whom Inho Kang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Stephen E. Robertson
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Graves
- Ilya Sutskever
- Tomas Mikolov
- Alex Krizhevsky
- Shaoqing Ren
- Yann LeCun
Who trusts Inho Kang overall
- Hamed Zamani
- Bernard Jim Jansen
- Dou Shen
- W. Bruce Croft
- Danielle L. Booth
- Hang Li
- Amanda Spink
- Jagdev Bhogal
- Zhiquan Ye
Papers from Inho Kang's perspective
Ordered by how strongly the researchers Inho Kang trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet Large Scale Visual Recognition Challenge
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Self-supervised pre-training and contrastive representation learning for multiple-choice video QA (2020) — 45 citations
- Semantic Sentence Matching with Densely-connected Recurrent and Co-attentive Information (2018) — 226 citations
- What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers (2021) — 134 citations
- Query type classification for web document retrieval (2003) — 305 citations