Ensemble deep learning: A review
Ensemble learning combines several individual models to obtain better generalization
performance. Currently, deep learning architectures are showing better performance …
performance. Currently, deep learning architectures are showing better performance …
A tutorial survey of architectures, algorithms, and applications for deep learning
L Deng - APSIPA transactions on Signal and Information …, 2014 - cambridge.org
In this invited paper, my overview material on the same topic as presented in the plenary
overview session of APSIPA-2011 and the tutorial material presented in the same …
overview session of APSIPA-2011 and the tutorial material presented in the same …
[图书][B] Automatic speech recognition
Automatic Speech Recognition (ASR), which is aimed to enable natural human–machine
interaction, has been an intensive research area for decades. Many core technologies, such …
interaction, has been an intensive research area for decades. Many core technologies, such …
Deep learning: methods and applications
This monograph provides an overview of general deep learning methodology and its
applications to a variety of signal and information processing tasks. The application areas …
applications to a variety of signal and information processing tasks. The application areas …
Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Most current speech recognition systems use hidden Markov models (HMMs) to deal with
the temporal variability of speech and Gaussian mixture models (GMMs) to determine how …
the temporal variability of speech and Gaussian mixture models (GMMs) to determine how …
Machine learning paradigms for speech recognition: An overview
L Deng, X Li - IEEE Transactions on Audio, Speech, and …, 2013 - ieeexplore.ieee.org
Automatic Speech Recognition (ASR) has historically been a driving force behind many
machine learning (ML) techniques, including the ubiquitously used hidden Markov model …
machine learning (ML) techniques, including the ubiquitously used hidden Markov model …
Scalable stacking and learning for building deep architectures
Deep Neural Networks (DNNs) have shown remarkable success in pattern recognition
tasks. However, parallelizing DNN training across computers has been difficult. We present …
tasks. However, parallelizing DNN training across computers has been difficult. We present …
[PDF][PDF] Three classes of deep learning architectures and their applications: a tutorial survey
L Deng - APSIPA transactions on signal and information …, 2012 - academia.edu
In this invited paper, my overview material on the same topic as presented in the plenary
overview session of APSIPA-2011 and the tutorial material presented in the same …
overview session of APSIPA-2011 and the tutorial material presented in the same …
Tensor deep stacking networks
A novel deep architecture, the tensor deep stacking network (T-DSN), is presented. The T-
DSN consists of multiple, stacked blocks, where each block contains a bilinear mapping …
DSN consists of multiple, stacked blocks, where each block contains a bilinear mapping …
The deep tensor neural network with applications to large vocabulary speech recognition
The recently proposed context-dependent deep neural network hidden Markov models (CD-
DNN-HMMs) have been proved highly promising for large vocabulary speech recognition. In …
DNN-HMMs) have been proved highly promising for large vocabulary speech recognition. In …