BinaryConnect： Training Deep Neural Networks with binary weights during propagations

时间 2020-12-20

标签深度学习繁體版

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1 Introduction 深度神经网络（DNN）大大推动了广泛的任务的state-of-the-art，特别是在语音识别[1,2]和计算机视觉方面，特别是图像中的物体识别[3,4]。最近，深度学习在自然语言处理方面取得重要进展，特别是统计机器翻译[5,6,7]。有趣的是，造成这一重大进展的关键因素之一是图形处理单元（GPU）的出现，加速10到30倍，从[8]开始，类似的改进与分布式

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1. BinaryConnect: Training Deep Neural Networks with binary weights during propagations 论文笔记
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. （转）A Recipe for Training Neural Networks
10. AlexNet--ImageNet Classification with Deep Convolutional Neural Networks
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1. BinaryConnect: Training Deep Neural Networks with binary weights during propagations 论文笔记
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. （转）A Recipe for Training Neural Networks
10. AlexNet--ImageNet Classification with Deep Convolutional Neural Networks

>>更多相关文章<<