Porting concepts from DNNs back to GMMs

By Kris Demuynck, Fabian Triefenbach. 2013

10 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. Dong Yu
3. Jinyu Li
4. Yoshua Bengio
5. Xiaodong He
6. Jianfeng Gao
7. Frank Torsten Bernd Seide
8. Yifan Gong

In Signal Processing

1. Li Deng
2. Dong Yu
3. Jinyu Li
4. Yifan Gong
5. Frank Torsten Bernd Seide
6. Yoshua Bengio
7. S. Renals
8. M. Seltzer

Citations in the corpus

• Deep Learning: Methods and Applications (2014, positive): “More recently, Demuynck and Triefenbach [70] developed the deep GMM architecture, where the aspects of DNNs that lead to strong performance are extracted and applied to build hierarchical GMMs.”
• Deep learning for limit order books (2016, unknown): “Various frameworks combining Gaussian mixtures with neural networks have been proposed by Variani et al. (2015), Demuynck and Triefenbach (2013), Paulik (2013), van den Oord and Schrauwen (2014), Yu and Seltzer (2011), Sainath et al. (2012), Deng and Chen (2014), and others.”