Tensor Deep Stacking Networks

By Brian Hutchinson, Li Deng, Dong Yu. 2013

151 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mathematics, Computer Vision and Pattern Recognition

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Citations in the corpus

Deep Learning: Methods and Applications (2014, methodology): “The above DSN architecture has recently been generalized to its tensorized version, which we call the tensor DSN (TDSN) [180, 181].”
Embedding Entities and Relations for Learning and Inference in Knowledge Bases (2014, background): “Further, tensor constructs have been usefully applied to some deep learning architectures (Yu et al., 2013; Hutchinson et al., 2013).”
A tutorial survey of architectures, algorithms, and applications for deep learning (2014, background): “That is, modules of the TDSN are stacking up in a similar way to form a deep architecture.”
Machine Learning Paradigms for Speech Recognition: An Overview (2013, background): “Examples include deep-structured CRF, tandem-MLP architecture [94], [258], deep convex or stacking network [248] and its tensor version [242], [243], [259], and detection-based ASR architecture [235], [236], [260].”
Sequence classification using the high-level features extracted from deep neural networks (2014, methodology): “The method of building deep stacking networks is a more general, recursive way of creating tandem-like features as the overall network built gets deeper and deeper [10][11][20][33].”
Big Data Deep Learning: Challenges and Perspectives (2014, methodology): “Most recently, a new deep architecture called Tensor Deep Stacking Network (T-DSN), which is based on the DSN, is implemented using CPU clusters for scalable parallel computing [45].”
Learning deep structured semantic models for web search using clickthrough data (2013, methodology): “Second, inspired by the deep learning framework recently shown to be highly successful in speech recognition [5][13][14][16][18], we extend the linear semantic models to their nonlinear counterparts using multiple hiddenrepresentation layers as.”
A deep convolutional neural network using heterogeneous pooling for trading acoustic invariance with phonetic confusion (2013, background): “extraction and recognition, scaling very well from small [8][26][27][28] to medium [3][4][19][36] and to large [2][6][17]”
A survey on deep learning for big data (2018, background): “Furthermore, a tensor deep stacking network was presented to improve the training efficiency further on CPU clusters [61].”
A Review of Deep Learning Models for Time Series Prediction (2019, methodology): “The above DSN has already been generalized to ten-sorized version, i.e., Tensor DSN (TDSN), which provides higher-order feature interactions comparing with conventional DSN [124].”
On the Expressive Power of Deep Learning: A Tensor Analysis (2015, unknown): “Socher et al. (2013), Yu et al. (2012), Setiawan et al. (2015), and Hutchinson et al. (2013) all proposed different neural network architectures that include tensor-based elements, and exhibit various advantages in terms of expressiveness and/or ease of training.”
A Deep Ensemble Learning Method for Monaural Speech Separation (2016, methodology): “The proposed method is also different from deep convex networks [6] and tensor deep stacking networks [16].”
Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community (2017, background): “Hutchinson et al.71 put forth Tensor-DSN as an efficient and parallel extension of DSNs for CPU clusters.”
Ensemble deep learning: A review (2021, methodology): “Comprehensive evaluation and the more detailed analysis of the learning algorithm and T-DSN implementation was given in [85].”
Recent advances in deep learning for speech research at Microsoft (2013, background): “…researchers at University of Toronto and other organizations, we at Microsoft have developed deep learning technology that has successfully replaced Gaussian mixtures for speech recognition and feature coding at an increasingly larger scale (e.g., [24][19][53][39][7][8][44][54][13][56][30][48]).”