Hierarchical Semi-supervised Classification with Incomplete Class Hierarchies
By Bhavana Dalvi, Aditya Kumar Mishra, William W. Cohen. 2016
20 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- William W. Cohen
- Zhilin Yang
- Ruslan Salakhutdinov
- Mohit Bansal
- Jianfeng Gao
- Quoc V. Le
- Percy Liang
- Yoshua Bengio
In Computer Vision and Pattern Recognition
- William W. Cohen
- Zhilin Yang
- Ruslan Salakhutdinov
- Mohit Bansal
- Luc Van Gool
- Quoc V. Le
- Dacheng Tao
- Alan L. Yuille
In Natural Language Processing
- Ruslan Salakhutdinov
- Zhilin Yang
- William W. Cohen
- Mohit Bansal
- Chenyi Zhuang
- Qiang Ma
- Percy Liang
- Jianfeng Gao
Citations in the corpus
- Revisiting Semi-Supervised Learning with Graph Embeddings (2016, methodology): “We compare our approach with label propagation (LP) (Zhu et al., 2003), semi-supervised embedding (SemiEmb) (Weston et al., 2012), manifold regularization (ManiReg) (Belkin et al., 2006), TSVM (Joachims, 1999), and graph embeddings (GraphEmb) (Perozzi et al., 2014).”
- Dual Graph Convolutional Networks for Graph-Based Semi-Supervised Classification (2018, background): “By linking the selected NELL entities (9,891 in total) with text descriptions in ClueWeb09 [7], each relation in NELL is described as a triplet ( e h , r , e t ) . e h and e t are the head and tail entity vectors, respectively, and r indicates the relation between them.”