Deep stacking networks for information retrieval

By Li Deng, Xiaodong He, Jianfeng Gao. 2013

81 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal 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

1. Li Deng
2. Jianfeng Gao
3. Dong Yu
4. Xiaodong He
5. Yoshua Bengio
6. Mohit Bansal
7. Tie-Yan Liu
8. Zhe Gan

In Signal Processing

1. Li Deng
2. Dong Yu
3. Xiaodong He
4. A. Acero
5. Jianfeng Gao
6. Jinyu Li
7. Yifan Gong
8. Rabab Kreidieh Ward

Citations in the corpus

• Deep Learning: Methods and Applications (2014, methodology): “In parallel with the IR studies reviewed above, the deep stacking network (DSN) discussed in Section 6 has also been explored recently for IR with insightful results [88].”
• A tutorial survey of architectures, algorithms, and applications for deep learning (2014, methodology): “[152] applies the DSN, as described in Section IV-A–C, to directly perform the task of learning-to-rank in information retrieval, based on a rich set of traditional features (e.”
• A Survey on Deep Learning (2018, background): “For a general information retrieval task, Deep Stacking Networks (DSNs) are proposed in [30].”
• Co-learning of Word Representations and Morpheme Representations (2014, unknown): “…in a number of NLP tasks (Col-lobert and Weston, 2008) (Glorot et al., 2011) (Mikolov et al., 2013a) (Mikolov et al., 2013b) (Socher et al., 2011) (Turney, 2013) (Turney and Pantel, 2010) (Weston et al., ) (Deng et al., 2013) (Collobert et al., 2011) (Mnih and Hinton, 2008) (Turian et al., 2010).”
• Knowledge-Powered Deep Learning for Word Embedding (2014, background): “Many studies follow this approach to improve and simplify text mining and NLP tasks [4–6, 8, 11, 19, 22, 20, 17, 10].”
• Using deep stacking network to improve structured compressed sensing with Multiple Measurement Vectors (2013, background): “Other applications of DSNs are introduced in [18] and [19].”
• Ensemble deep learning: A review (2021, methodology): “2013 Deep stacking networks for information retrieval [206] Stacking Information Retrieval 2013 Regularization of Neural Networks using DropConnect [105] Implicit ensemble Image recognition”
• Learning deep structured semantic models for web search using clickthrough data (2013, background): “Popular models can be grouped into two categories, linear projection models and generative topic models, which we will review in turn.”