这种现象就被称为梯度爆炸,经常发生在循环神经网络中。 上述的两种情况其实存在了很多年,一直到2010年Xavier Glorot 和 Yoshua Bengio在他们的论文<Understanding the Difficulty of Training Deep Feedforward Neural etworks> 中提出了一些关于当时神经网络产生梯度弥散现象的看法。在当时,大家普遍使用Sigmoid函数作为激活...
原文:Training Deep Neural Networks on Noisy Labels with Bootstrapping 摘要 目前最先进的用于视觉物体识别和检测的深度学习系统使用纯粹的监督训练,并使用正则化,如dropout来避免过度拟合。性能关键取决于标记的例子的数量,在目前的实践中,标签被假定为是无歧义的和准确的。然而,这一假设往往不成立;例如,在识别中,...
我们表明,可以在MNIST上训练多层感知器(MLP),在CIFAR-10和SVHN上使用BinaryNet训练ConvNet,并获得几乎最先进的结果。在运行时,BinaryNet大大减少了内存使用,并用1位异或(XNOR)运算代替了大多数乘法运算,这可能对通用和专用深度学习硬件产生重大影响。我们编写了一个二值矩阵乘法GPU内核,使用它运行我们的MNIST MLP比使...
BinaryConnect: Training Deep Neural Networks with binary weights during propagations 论文笔记,程序员大本营,技术文章内容聚合第一站。
首先，初始化是关键。通过Glorot和He初始化策略，可以确保每一层输入输出方差相等，避免信号的消失或饱和，确保训练过程的稳定性。其次，激活函数的选择同样重要。非饱和激活函数如Leaky ReLU、PReLU、ELU或SELU，可以有效防止梯度消失和爆炸问题。BatchNormalization是另一种有效策略，它通过标准化层的输入来...
So far in this module, you've learned a lot about the theory and principles of deep learning with neural networks. The best way to learn how to apply this theory is to actually build a deep learning model, and that's what you'll do in this exercise....
Machine learning (ML), particularly applied to deep neural networks via the backpropagation algorithm, has enabled a wide spectrum of revolutionary applications ranging from the social to the scientific1,2. Triumphs include the now everyday deployment of handwriting and speech recognition through to ap...
The use of a pipelined algorithm that performs parallelized computations to train deep neural networks (DNNs) for performing data analysis may reduce training time. The DNNs may be one of context-independent DNNs or context-dependent DNNs. The training may include partitioning training data into ...
Adversarial examples 是在这篇文章首次提出的,Intriguing properties of neural networks(大家可以下载看看原文)官方话就是:在数据集中通过故意添加细微的干扰所形成输入样本,受干扰之后的输入导致模型以高置信度给出了一个错误的输出。 注意细微的干扰,也就是这种干扰对于人眼是可以忽略的,这才叫细微的干扰。
Training Deep Neural Networks Just as we don’t haul around all our teachers, a few overloaded bookshelves and a red-brick schoolhouse to read a Shakespeare sonnet, inference doesn’t require all the infrastructure of its training regimen to do its job well. ...