Jongseok Kim
Computer Vision and Pattern Recognition · 2018
A researcher in the Science corpus: 1 paper; trusts 154 researchers and is trusted by 34 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 34 citations into it; #4,986 of 21,575 by trust here, #7,499 by citations
- Artificial Intelligence — 21 citations into it; #8,878 of 35,345 by trust here, #10,667 by citations
- Multimodal Learning — 18 citations into it; #305 of 1,211 by trust here, #610 by citations
- Natural Language Processing — 12 citations into it
- Model Architectures — 7 citations into it
Whom Jongseok Kim trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Richard Socher
- Diederik P. Kingma
- Kaiming He
- Geoffrey E. Hinton
- Sepp Hochreiter
- Sergey Ioffe
- Alex Krizhevsky
- Yoshua Bengio
- Christian Szegedy
- Jimmy Ba
Who trusts Jongseok Kim overall
- Antoine Miech
- Linchao Zhu
- Jie Lei
- Huaishao Luo
- Linjie Li
- Po-Yao (Bernie) Huang
- Alex Wang
- Jianwei Yang
- Zineng Tang
- Xiaohan Wang
Papers from Jongseok Kim's perspective
Ordered by how strongly the researchers Jongseok Kim 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
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
Their papers
- A Joint Sequence Fusion Model for Video Question Answering and Retrieval (2018) — 405 citations