Jinseok Nam
Artificial Intelligence · 2013–2017
A researcher in the Science corpus: 3 papers; trusts 31 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 38 citations into it
- Computer Vision and Pattern Recognition — 14 citations into it
- Natural Language Processing — 14 citations into it
- Optimization & Training — 10 citations into it
- Information Systems — 8 citations into it
- Evaluation & Benchmarks — 7 citations into it
Whom Jinseok Nam trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Ilya Sutskever
- Diederik P. Kingma
- Thorsten Joachims
- Yoshua Bengio
- Alex Krizhevsky
- Tomas Mikolov
- Yann LeCun
- Jimmy Ba
- Kyunghyun Cho
Who trusts Jinseok Nam overall
- Weiwei Liu
- Jack Lanchantin
- Zhen Wang
- Amaia Salvador
- Haobo Wang
- Tong Wei
- Ronghui You
- Chung-Wei Lee
- Sean Welleck
- Emanuel Ben Baruch
Papers from Jinseok Nam's perspective
Ordered by how strongly the researchers Jinseok Nam trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Large-Scale Multi-label Text Classification - Revisiting Neural Networks (2013) — 384 citations
- Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification (2017) — 184 citations
- Using semantic similarity for multi-label zero-shot classification of text documents (2016) — 54 citations