BinaryConnect： Training Deep Neural Networks with binary weights during propagations
1 Introduction 深度神經網絡（DNN）大大推動了廣泛的任務的state-of-the-art，特別是在語音識別[1,2]和計算機視覺方面，特別是圖像中的物體識別[3,4]。最近，深度學習在自然語言處理方面取得重要進展，特別是統計機器翻譯[5,6,7]。有趣的是，造成這一重大進展的關鍵因素之一是圖形處理單元（GPU）的出現，加速10到30倍，從[8]開始，類似的改進與分佈式
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相關文章
- 1. BinaryConnect: Training Deep Neural Networks with binary weights during propagations 論文筆記
- 2. Binarized Neural Networks:Training Deep Neural Networks with Weights and Activations -1,1
- 3. Balanced Binary Neural Networks With Gated Residual
- 4. TRAINING DEEP NEURAL NETWORKS WITH LOW PRECISION MULTIPLICATIONS
- 5. [cv231n] Lecture 7 | Training Neural Networks II
- 6. AlexNet：ImageNet Classification with Deep Convolutional Neural Networks
- 7. ImageNet Classification with Deep Convolutional Neural Networks
- 8. Improving Deep Neural Networks
- 9. （轉）A Recipe for Training Neural Networks
- 10. AlexNet--ImageNet Classification with Deep Convolutional Neural Networks