Resource Constrained Model Compression via Minimax Optimization for Spiking Neural Networks

Author:

Chen Jue1ORCID,Yuan Huan2ORCID,Tan Jianchao2ORCID,Chen Bin2ORCID,Song Chengru2ORCID,Zhang Di2ORCID

Affiliation:

1. Peking University, Beijing, China

2. Kuaishou Technology, Beijing, China

Publisher

ACM

Reference84 articles.

1. Guillaume Bellec , Darjan Salaj , Anand Subramoney , Robert Legenstein , and Wolfgang Maass . 2018. Long short-term memory and learning-to-learn in networks of spiking neurons. Advances in neural information processing systems , Vol. 31 ( 2018 ). Guillaume Bellec, Darjan Salaj, Anand Subramoney, Robert Legenstein, and Wolfgang Maass. 2018. Long short-term memory and learning-to-learn in networks of spiking neurons. Advances in neural information processing systems , Vol. 31 (2018).

2. Yoshua Bengio , Nicholas Léonard , and Aaron Courville . 2013. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432 ( 2013 ). Yoshua Bengio, Nicholas Léonard, and Aaron Courville. 2013. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432 (2013).

3. Han Cai , Chuang Gan , Tianzhe Wang , Zhekai Zhang , and Song Han . 2019 . Once-for-All: Train One Network and Specialize it for Efficient Deployment . In International Conference on Learning Representations. Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, and Song Han. 2019. Once-for-All: Train One Network and Specialize it for Efficient Deployment. In International Conference on Learning Representations.

4. Spiking Deep Convolutional Neural Networks for Energy-Efficient Object Recognition

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