论文翻译NeuralNetworksWithFewMultiplications, 众所周知,对于大多数深度学习算法而言,训练是非常耗时的。 由于训
code:https://github.com/hantek/BinaryConnect 编辑:Daniel 本文提出的量化方法分为两步,第一步在前向传播中,对权重进行随即二值化或三值化,第二步在反向传播中将每层的输入x量化成2的N次方,将乘法操作转换为位移操作。 其中w'为网络中原始的全精度权重,W为随机二值化后的权重。为了使W为1的概率在合理的区...
Lin, Z., Courbariaux, M., Memisevic, R., Bengio, Y.: Neural networks with few multiplications. In: Proc. Int. Conf. Learn. Represent. (2016) 2 37. Liu, L., Shao, L., Shen, F., Yu, M.: Discretely coding semantic rank orders for image hashing. In: Proceeding of the IEEE ...
BinaryConnect: Training Deep Neural Networks with binary weights during propagations. You may want to checkout our subsequent work: Neural Networks with Few Multiplications BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1 Requirements Python, Numpy, Scipy ...
Neural Networks with Few Multiplications For most deep learning algorithms training is notoriously time consuming. Since most of the computation in training neural networks is typically spent on f... Z Lin,M Courbariaux,R Memisevic,... 被引量: 163发表: 2015年 Training Neural Networks with Thres...
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sub-photon-per-multiplication demonstration—noise reduction from the accumulation of scalar multiplications in dot-product sums—is applicable to many different optical-neural-network architectures. Our work shows that optical neural networks can achieve accurate results using extremely low optical energies....
Neural Networks with Few Multiplications. In Proceedings of the 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, 2–4 May 2016. [Google Scholar] Ni, R.; Chu, H.; Castañeda, O.; Chiang, P.; Studer, C.; Goldstein, T. WrapNet: Neural Net ...
Convolutional neural networks (CNNs) have demonstrated remarkable performance in many areas but require significant computation and storage resources. Quantization is an effective method to reduce CNN complexity and implementation. The main research objective is to develop a scalable quantization algorithm fo...
But, more important than the name was the idea - that neural networks with many layers really could be trained well, if the weights are initialized in a clever way rather than randomly. Hinton once expressed the need for such an advance at the time: “Historically, this was very important...