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…
原文: 二值神经网络(Binary Neural Network,BNN) 在我刚刚过去的研究生毕设中,我在ImageNet数据集上验证了图像特征二值化后仍然具有很强的表达能力,可以在检索中达到较好的效果.而Bengio大神的这篇文章,则不止于将特征二值化,而是要将权重和每层的激活值统统二值化.相比于非二值化的网络,将大量的数学运算变成了位操作.这样就节省了大量的空间而前向传播的时间,使神经网络的应用门槛变得更低. 本文是阅读Bengio二值化网络文章的笔记,特此声明. 要想使整个神经网络二值化…
转载请注明出处: http://www.cnblogs.com/sysuzyq/p/6248953.html by 少侠阿朱…
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 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…
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…
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…
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…
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…
About this Course This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good res…
论文:Deep Neural Networks for YouTube Recommendations 发表时间:2016 发表作者:(Google)Paul Covington, Jay Adams, Emre Sargin 发表刊物/会议:RecSys 论文链接:论文链接 这篇论文是google的YouTube团队在推荐系统上DNN方面的尝试,发表在16年9 月的RecSys会议.本文就focus在YouTube视频推荐的DNN算法,文中不但详细介绍了Youtube推荐算法和架构细节,还给了…
论文地址:https://asa.scitation.org/doi/abs/10.1121/1.5036725 深度神经网络在浅水环境中的源定位 摘要: 深度神经网络(DNNs)在表征复杂的非线性关系方面具有优势.本文将DNNs应用于浅水环境下的源定位.提出了两种方法,通过不同的神经网络结构来估计宽带源的范围和深度.第一阶段采用经典的两阶段方案,特征提取和DNN分析是两个独立的步骤;与模态信号空间相关联的特征向量被提取为输入特征.然后,利用时滞神经网络对长期特征表示进行建模,构建回归模型;第二…
Mastering the game of Go with deep neural networks and tree search Nature 2015  这是本人论文笔记系列第二篇 Nature 的文章了,第一篇是 DQN.好紧张!好兴奋! 本文可谓是在世界上赚够了吸引力! 围棋游戏被看做是 AI 领域最有挑战的经典游戏,由于其无穷的搜索空间 和 评价位置和移动的困难.本文提出了一种新的方法给计算机来玩围棋游戏,即:利用 "value network" 来评价广泛的位置 和 “p…
论文笔记-IGCV3:Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks 2018年07月11日 14:05:46 Liven_Zhu 阅读数 846   介绍 在这篇论文中,作者同时使用低秩核和稀疏核(low-rank and sparse kernel)来组成一个密集kernel.基于ICGV2的基础上,作者提出了ICGV3. 近几年,卷积网络在计算机视觉上的有效性已经得到了验证.目前卷积网络的…
目录 概 主要内容 LSGD Box 初始化 Box for Resnet 代码 Cyr E C, Gulian M, Patel R G, et al. Robust Training and Initialization of Deep Neural Networks: An Adaptive Basis Viewpoint.[J]. arXiv: Learning, 2019. @article{cyr2019robust, title={Robust Training and Initi…
文章:Clustering Convolutional Kernels to Compress Deep Neural Networks 链接:http://openaccess.thecvf.com/content_ECCV_2018/papers/Sanghyun_Son_Clustering_Kernels_for_ECCV_2018_paper.pdf 这篇文章主要是研究模型的压缩和加速.其他的文章大多数都只研究网络结构中的冗余参数或影响不大的结构,用剪枝的方法来压缩模型.作者从另一个方…
论文地址:MelGAN:条件波形合成的生成对抗网络 代码地址:https://github.com/descriptinc/melgan-neurips 音频实例:https://melgan-neurips.github.io/ 配有MelGAN解码器的音乐翻译网络:https://www.descript.com/overdub 摘要 以前的工作(Donahue等人,2018a:Engel等人,2019a)已经发现用GAN生成相干的原始音频波形是一个挑战.在本文中,我们证明了通过引入一系列结…
Must Know Tips/Tricks in Deep Neural Networks (by Xiu-Shen Wei)   Deep Neural Networks, especially Convolutional Neural Networks (CNN), allows computational models that are composed of multiple processing layers to learn representations of data with…
http://lamda.nju.edu.cn/weixs/project/CNNTricks/CNNTricks.html Deep Neural Networks, especially Convolutional Neural Networks (CNN), allows computational models that are composed of multiple processing layers to learn representations of data with mul…
Deep Neural Network - Application Congratulations! Welcome to the fourth programming exercise of the deep learning specialization. You will now use everything you have learned to build a deep neural network that classifies cat vs. non-cat images. In…
1,概述 模型量化属于模型压缩的范畴,模型压缩的目的旨在降低模型的内存大小,加速模型的推断速度(除了压缩之外,一些模型推断框架也可以通过内存,io,计算等优化来加速推断). 常见的模型压缩算法有:量化,剪枝,蒸馏,低秩近似以及紧凑模型设计(如mobileNet)等操作.但在这里有些方法只能起到缩减模型大小,而起不到加速的作用,如稀疏化剪枝.而在现代的硬件设备上,其实更关注的是模型推断速度.今天我们就讲一种既能压缩模型大小,又能加速模型推断速度:量化. 量化一般可以分为两种模式:训练后的量化(po…
原论文出处:https://www.nature.com/articles/nature14539 by Yann LeCun, Yoshua Bengio & Geoffrey Hinton Nature volume521, pages436–444 (28 May 2015) 译者:这篇论文性质为深度学习的综述,原本只是想做做笔记,但找到的翻译都不怎么通顺.既然要啃原文献,索性就做个翻译,尽力准确通畅.转载使用请注明本文出处,当然实在不注明我也并没有什么办法. 论文中大量使用貌似作者默认术…
论文地址:DCCRN:用于相位感知语音增强的深度复杂卷积循环网络 论文代码:https://paperswithcode.com/paper/dccrn-deep-complex-convolution-recurrent-1 引用:Hu Y,Liu Y,Lv S,et al. DCCRN: Deep complex convolution recurrent network for phase-aware speech enhancement[J]. arXiv preprint arXiv:…
论文地址:TinyLSTMs:助听器的高效神经语音增强 音频地址:https://github.com/Bose/efficient-neural-speech-enhancement 引用格式:Fedorov I,Stamenovic M,Jensen C,et al. TinyLSTMs:Efficient neural speech enhancement for hearing aids[J]. arXiv preprint arXiv:2005.11138,2020. 摘要 现代语音增…
On Explainability of Deep Neural Networks « Learning F# Functional Data Structures and Algorithms is Out!   On Explainability of Deep Neural Networks During a discussion yesterday with software architect extraordinaire David Lazar regarding how every…
Introduction to Deep Neural Networks Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw…
Deep Neural Networks are the more computationally powerful cousins to regular neural networks. Learn exactly what DNNs are and why they are the hottest topic in machine learning research. The term deep neural network can have several meanings, but on…
Classifying plankton with deep neural networks The National Data Science Bowl, a data science competition where the goal was to classify images of plankton, has just ended. I participated with six other members of my research lab, the Reservoir lab o…
(Deep) Neural Networks (Deep Learning) , NLP and Text Mining 最近翻了一下关于Deep Learning 或者 普通的Neural Network在NLP以及Text Mining方面应用的文章,包括Word2Vec等,然后将key idea提取出来罗列在了一起,有兴趣的可以下载看看: http://pan.baidu.com/s/1sjNQEfz 我没有把一些我自己的想法放到里面,大家各抒己见,多多交流. 下面简单概括一些其中的几篇p…
声明:所有内容来自coursera,作为个人学习笔记记录在这里. Initialization Welcome to the first assignment of "Improving Deep Neural Networks". Training your neural network requires specifying an initial value of the weights. A well chosen initialization method will help…