Pruning deep neural networks by optimal brain damage

By Chao Liu, Zhiyong Zhang, Dong Wang. 2014

28 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Efficiency & Compression, Optimization & Training

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. Jinyu Li
2. Yifan Gong
3. Dong Yu
4. Li Deng
5. Shauli Ravfogel
6. Yoav Goldberg
7. Yongqiang Wang
8. Mohit Bansal

In Efficiency & Compression

1. Jinyu Li
2. Yifan Gong
3. Elad Ben-Zaken
4. Yoav Goldberg
5. Mehdi Ghatee
6. Shauli Ravfogel
7. Mohammad Mahdi Bejani
8. Dong Yu

In Optimization & Training

1. Jinyu Li
2. Yifan Gong
3. Elad Ben-Zaken
4. Yoav Goldberg
5. Mehdi Ghatee
6. Yongqiang Wang
7. Shauli Ravfogel
8. Mohammad Mahdi Bejani

Citations in the corpus

• BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models (2021, methodology): “more parameters than needed in inference (Buciluǎ et al., 2006; Hinton et al., 2015; Urban et al., 2017; Karnin, 1990; Reed, 1993; Augasta and Kathirvalavakumar, 2013; Liu et al., 2014; Han et al., 2015; Molchanov et al., 2017).”
• A systematic review on overfitting control in shallow and deep neural networks (2021, methodology): “To control the complexity of a model, there are different methods, see e.g., (Chen and Yao 2009; Li et al. 2018; Zhu et al. 2018; Liu et al. 2014; Eigenmann and Nossek 1999; Finnoff et al. 1993).”
• Small-footprint high-performance deep neural network-based speech recognition using split-VQ (2015, background): “Index Terms— DNN, on device speech recognition, model compression, split-VQ”