论文翻译：BinaryConnect: Training Deep Neural Networks with binary weights during propagations

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论文翻译：BinaryConnect: Training Deep Neural Networks with binary weights during propagations

目录摘要 1.引言 2.BinaryConnect 2.1 +1 or -1 2.2确定性与随机性二值化 2.3 Propagations vs updates 2.4 Clipping 2.5 A few more tricks 2.6 Test-Time Inference 3 Benchmark results 3.1 Permutation-invariant MNIST 3.2 CIFAR-10 3.3 SVHN 4 Related works 5. Conclusion and f…

Training Deep Neural Networks

http://handong1587.github.io/deep_learning/2015/10/09/training-dnn.html //转载于 Training Deep Neural Networks Published: 09 Oct 2015 Category: deep_learning Tutorials Popular Training Approaches of DNNs — A Quick Overview https://medium.com/@asjad/p…

Training (deep) Neural Networks Part: 1

Training (deep) Neural Networks Part: 1 Nowadays training deep learning models have become extremely easy with high-quality libraries such as Torch and Theano. These libraries are really helpful for rapidly prototyping deep learning models even witho…

CVPR 2018paper: DeepDefense: Training Deep Neural Networks with Improved Robustness第一讲

前言:好久不见了,最近一直瞎忙活,博客好久都没有更新了,表示道歉.希望大家在新的一年中工作顺利,学业进步,共勉! 今天我们介绍深度神经网络的缺点:无论模型有多深,无论是卷积还是RNN,都有的问题:以图像为例,我们人为的加一些东西,然后会急剧的降低网络的分类正确率.比如下图: 在生成对抗样本之后,分类器把alps 以高置信度把它识别成了狗,下面的一幅图,是把puffer 加上一些我们人类可能自己忽视的东西,但是对分类器来说,这个东西可能很重要,这样分类器就会去调节它,这就导致分类器以百分之百的置信…

论文翻译：BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or −1

目录摘要引言 1.BinaryNet 符号函数梯度计算和累积通过离散化传播梯度一些有用的成分算法1 使用BinaryNet训练DNN 算法2 批量标准化转换(Ioffe和Szegedy,2015),适用于小批量激活x. 算法3 ADAM学习规则(Kingma&Ba,2014). 2.基准测试结果 MLP on MNIST ConvNet on CIFAR-10 ConvNet on SVHN 3.在运行时更快第一层 4.相关工作结论参考资料论文地址:https://arxiv…

为什么深度神经网络难以训练Why are deep neural networks hard to train?

Imagine you're an engineer who has been asked to design a computer from scratch. One day you're working away in your office, designing logical circuits, setting out AND gates, OR gates, and so on, when your boss walks in with bad news. The customer h…

This instability is a fundamental problem for gradient-based learning in deep neural networks. vanishing exploding gradient problem

The unstable gradient problem: The fundamental problem here isn't so much the vanishing gradient problem or the exploding gradient problem. It's that the gradient in early layers is the product of terms from all the later layers. When there are many…