Solving Nonlinear Estimation Problems Using Splines

By Dong Yu, Li Deng. 2009

18 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.

Citations in the corpus

Deep Learning: Methods and Applications (2014, methodology): “A variant of this approach has been more recently developed using different learning techniques for time-varying HMM parameters and with the applications extended to speech recognition robustness (Yu and Deng, 2009; Yu et al., 2009a).”
A tutorial survey of architectures, algorithms, and applications for deep learning (2014, methodology): “A variant of this approach has beenmore recently developed using different learning techniques for time-varying HMM parameters and with the applications extended to speech recognition robustness [60, 61].”
Calibration of Confidence Measures in Speech Recognition (2011, methodology): “We evaluate the proposed techniques on several datasets in Section VI and conclude the paper in Section VII.”
An Overview of Deep-Structured Learning for Information Processing (2011, methodology): “A core component of the DBN is a greedy, layer-by-layer learning algorithm which optimizes DBN weights at time complexity linear to the size and depth of the networks.”
Learning in the Deep-Structured Conditional Random Fields (2009, unknown): “…í µí±í µí± í µí±¦ í µí±¡−1 , í µí±¦ í µí±¡ , í µí², í µí±¡ = í µí± í µí± í µí± í µí± í µí±¦ í µí±¡−1 , í µí±¦ í µí±¡ , í µí², í µí±¡ í µí± í µí± í µí±¦ í µí±¡−1 , í µí±¦ í µí±¡ , í µí², í µí±¡ , (5) where í µí± í µí± . is a weight function whose definition can be found in [8][10][11].”
Language recognition using deep-structured conditional random fields (2010, methodology): “where is a weight function whose definition and calculation method can be found in [9][10][11] and the number needs to be determined based on the amount of training data available.”
Sequential Labeling Using Deep-Structured Conditional Random Fields (2010, methodology): “achieved by expanding each continuous feature into several continuous features as discussed in [28] with the cubic spline approximation techniques developed in [25] and [29].”
Investigation of full-sequence training of deep belief networks for speech recognition (2010, methodology): “…techniques in speech recognition in the DBN framework, such as the use of object functions better correlating with errors than MMI [7], use of context dependency [1][2], and conditioning the DBN weights on specific auxiliary factors that control the variability in the speech data [21][22].”
Binary coding of speech spectrograms using a deep auto-encoder (2010, background): “Also, it is important to condition the discriminative deep auto-encoder’s parameters on specific variability factors (e.g., [2][13][14]).”