【論文閱讀】韓鬆《Efficient Methods And Hardware For Deep Learning》節選《Learning both Weights and Connections 》
Pruning Deep Neural Networks 本節內容主要來自NIPS 2015論文《Learning both Weights and Connections for Efficient Neural Networks》。 這部分主要介紹如何剪枝網絡，及如何迭代訓練已剪枝的網絡以保證準確率。同時提供方法證明剪枝模型帶來的加速和能源效率提升。 剪枝方法 訓練模型，training con
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相關文章
- 1. 【論文閱讀】韓鬆《Efficient Methods And Hardware For Deep Learning》節選《Deep compression》
- 2. 【韓鬆博士畢業論文】Efficient methods and hardware for deep learning
- 3. 論文《Learning both Weights and Connections for Efficient Neural Network》閱讀筆記
- 4. 韓鬆博士畢業論文Efficient methods and hardware for deep learning論文詳解
- 5. 論文品讀：Learning both Weights and Connections for Efficient Neural Networks
- 6. 【Learning both Weights and Connections for Efficient Neural Networks】論文筆記
- 7. 《Learning both Weights and Connections for Efficient Neural Networks》論文筆記
- 8. CS231n Lecture 15 | Efficient Methods and Hardware for Deep Learning
- 9. 網絡模型剪枝-論文閱讀《Learning both Weights and Connections for Efficient Neural Networks》
- 10. 深度網絡推理加速（Learning both Weights and Connections for Efficient Neural Networks）