Learning in the Deep-Structured Conditional Random Fields

By Dong Yu, Li Deng, Shizhen Wang. 2009

38 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Li Deng
2. Dong Yu
3. Yoshua Bengio
4. G. Wang
5. M. Seltzer
6. Abdel-rahman Mohamed
7. Jinyu Li
8. Geoffrey E. Hinton

Citations in the corpus

• Deep Learning-Based Classification of Hyperspectral Data (2014, background): “These deep architectures have been shown to yield promising performance in many field including classification or regression tasks that involve image [26], [27], [47], language [28], and speech [29].”
• Binary coding of speech spectrograms using a deep auto-encoder (2010, background): “…has led to renewed interest in exploring deep, multi-layer networks for a number of machine learning problems including encoding [8][9], retrieval [12], as well as the problems associated with classification and regression that involves image [8][9], language [16][17] and speech [10][11][15].”
• Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network (2015, unknown): “The applications of deep learning to image classification [32], [33], language processing [34] and speech recognition [35] have been actively studied in recent years.”
• Deep-structured hidden conditional random fields for phonetic recognition (2010, background): “proposed the deep-structured conditional random fields (DCRFs) [17] and obtained better than the state-of-the-art results on the query labeling [4] tasks.”
• Language recognition using deep-structured conditional random fields (2010, methodology): “After the intermediate layers are pre-trained layer by layer using the unsupervised approach we just described, the model parameters are jointly fine-tuned using the back propagation to optimize the sequential conditional log-likelihood �,�:�&�· �:�; [ 14 ].”
• Investigation of full-sequence training of deep belief networks for speech recognition (2010, methodology): “This new model can be seen as a modification of the deep-structured CRF of [19][20] where the lower multiple layers of CRFs are replaced by DBNs.”
• Stock prediction using deep learning (2016, background): “However, deep architectures can overcome these problems [14] and they have already yielded promising performance in many fields including language [30], speech [18] and image [8, 10, 34].”