Parallel Training for Deep Stacking Networks

By Li Deng, Brian Hutchinson, Dong Yu. 2012

24 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. Quoc V. Le
2. Yoshua Bengio
3. Mohit Bansal
4. Kyunghyun Cho
5. Jianfeng Gao
6. Ruslan Salakhutdinov
7. Dong Yu
8. Dacheng Tao

In Signal Processing

1. Dong Yu
2. Andrew W. Senior
3. Quoc V. Le
4. Jinyu Li
5. Li Deng
6. Geoffrey E. Hinton
7. Heiga Zen
8. Matthew D. Zeiler

Citations in the corpus

• Hierarchical Convolutional Deep Learning in Computer Vision (2013, background): “This challenge continues to motivate researchers to design better unsupervised algorithms [95, 107] and better optimization methods for training deep neural nets [29,63].”
• A Review of Deep Learning Models for Time Series Prediction (2019, background): “Given the fact that x i denote the training vector), the weights in DSN can be learned by simple gradient computation with batch training and parallel strategy [123].”
• On rectified linear units for speech processing (2013, background): “This challenge continued to motivate researchers to design better unsupervised algorithms [3, 4] and better optimization methods for training deep neural nets [5, 6].”