Jingu Kim
Computer Vision and Pattern Recognition · 2008–2013
A researcher in the Science corpus: 3 papers; trusts 48 researchers and is trusted by 30 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 27 citations into it; #8,260 of 21,575 by trust here, #16,787 by citations
- Signal Processing — 15 citations into it; #1,536 of 7,572 by trust here, #3,309 by citations
- Computational Theory and Mathematics — 13 citations into it
- Computational Mechanics — 11 citations into it; #2,045 of 10,001 by trust here, #4,178 by citations
- Artificial Intelligence — 10 citations into it
- Molecular Biology — 9 citations into it
- Optimization & Training — 9 citations into it
- Representation & Self-Supervised Learning — 7 citations into it; #1,202 of 3,145 by trust here, #2,805 by citations
Whom Jingu Kim trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Daniel D. Lee
- Andrzej S. Cichocki
- Hyunsoo Kim
- Chih-Jen Lin
- Haesun Park
- P. Paatero
- Chris H. Q. Ding
- Karthik Devarajan
- Jean-Philippe Brunet
- Patrik O. Hoyer
Who trusts Jingu Kim overall
- Nicolas Gillis
- Haesun Park
- Madeleine Udell
- Ramakrishnan Kannan
- Ioakeim Perros
- Prateek Jain
- Kais Allab
- Pierre-André Savalle
- J. Choo
- Aurko Roy
Papers from Jingu Kim's perspective
Ordered by how strongly the researchers Jingu Kim trusts endorse each paper. Same corpus, a different order for every researcher.
- Gradient-based learning applied to document recognition
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning the parts of objects by non-negative matrix factorization
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Algorithms for nonnegative matrix and tensor factorizations: a unified view based on block coordinate descent framework (2013) — 375 citations
- Fast Nonnegative Matrix Factorization: An Active-Set-Like Method and Comparisons (2011) — 353 citations
- Sparse Nonnegative Matrix Factorization for Clustering (2008) — 247 citations