两种非常常见的非线性单元:rectified linear units (ReLUs) 和 leaky ReLUs 我们选取binary hinge loss进行二分类 对于多分类,我们可以定义multiclass hinge loss 定义Ω为网络的参数空间, L(ω)为loss. 由于我们选了ReLU非线性单元作为loss, 那么L(ω)是分片线性的.对于参数空间,我们可以将其进行一个划分, 分成有限个open cells Ωu 和 边界N,则损失函数L(ω)在cell的内部是光滑的,在边界…

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…

DEEP LEARNING WITH STRUCTURE Charlie Tang is a PhD student in the Machine Learning group at the University of Toronto, working with Geoffrey Hinton and Ruslan Salakhutdinov, whose research interests include machine learning, computer vision and cogni…

Deep L-layer neural network 1 - General methodology As usual you will follow the Deep Learning methodology to build the model: 1). Initialize parameters / Define hyperparameters 2). Loop for num_iterations: a. Forward propagation b. Compute cost func…

In recent years, there’s been a resurgence in the field of Artificial Intelligence. It’s spread beyond the academic world with major players like Google, Microsoft, and Facebook creating their own research teams and making some impressive acquisition…

Survey Recent Advances in Efficient Computation of Deep Convolutional Neural Networks, [arxiv '18] A Survey of Model Compression and Acceleration for Deep Neural Networks [arXiv '17] Quantization The ZipML Framework for Training Models with End-to-En…

Convolutional Neural Networks ImageNet Models Architecture Design Activation Functions Visualization Fast Convolution Low-Rank Filter Approximation Low Precision Parameter Pruning Transfer Learning Theory 3D Data Hardware ImageNet Models 2017 CVPR Xc…

CVPR2017 paper list Machine Learning 1 Spotlight 1-1A Exclusivity-Consistency Regularized Multi-View Subspace Clustering Xiaojie Guo, Xiaobo Wang, Zhen Lei, Changqing Zhang, Stan Z. Li Borrowing Treasures From the Wealthy: Deep Transfer Learning Thro…

Byte Tank Posts Archive Deep Reinforcement Learning: Playing a Racing Game OCT 6TH, 2016 Agent playing Out Run, session 201609171218_175epsNo time limit, no traffic, 2X time lapse Above is the built deep Q-network (DQN) agent playing Out Run, trained…

在做东西的时候用到了社区发现,因此了解了一下有关社区发现的一些问题 1,社区发现算法 (1)SCAN:一种基于密度的社团发现算法 Paper: <SCAN: A Structural Clustering Algorithm for Networks>  Auther: Xiaowei Xu, Nurcan Yuruk, Zhidan Feng, Thomas A. J. Schweiger  Conference: SIGKDD 2007 主要概念: 节点相似度定义为两个节点共同邻居的数目与…

两派 1. 新的卷机计算方法 这种是直接提出新的卷机计算方式,从而减少参数,达到压缩模型的效果,例如SqueezedNet,mobileNet SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size 修改网络结构,类似于mobileNet MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Appli…

1. Parameter pruning and sharing 1.1 Quantization and Binarization Compressing deep convolutional networks using vector quantization Quantized convolutional neural networks for mobile devices Improving the speed of neural networks on cpus Deep learni…

一.概念 (1)完全子图/全耦合网络/k-派系:所有节点全部两两相连 图1 这些全耦合网络也成为派系,k-派系表示该全耦合网络的节点数目为k 1)k-派系相邻:两个不同的k-派系共享k-1个节点,认为他们相邻 2)k-派系连通:一个k-派系可以通过若干个相邻的k-派系到达另一个k-派系,则称这两个k-派系彼此联通 二.思路 图2 1- first find all cliques of size k in the graph 第一步首先找到网络中大小为K的完全子图,例如图2中k=3的完全子图有{…

先睹为快:神经网络顶会ICLR 2019论文热点分析 - lqfarmer的文章 - 知乎 https://zhuanlan.zhihu.com/p/53011934 作者:lqfarmer链接:https://zhuanlan.zhihu.com/p/53011934来源:知乎著作权归作者所有.商业转载请联系作者获得授权,非商业转载请注明出处. ICLR-2019(International Conference on Learning Representations 2019),将于2019…

深度自适应增量学习(Incremental Learning Through Deep Adaptation) 2018-05-25 18:56:00 木呆呆瓶子 阅读数 10564  收藏 更多 分类专栏: 算法学习 增量学习   版权声明:本文为博主原创文章,遵循CC 4.0 BY-SA版权协议,转载请附上原文出处链接和本声明. 本文链接:https://blog.csdn.net/qq_33880788/article/details/80455714 翻译论文:Incremental L…

我们相信开发自动驾驶技术是我们这个时代最大的工程挑战之一,行业和研究团体之间的合作将扮演重要角色.由于这个原因,我们一直在通过参加学术会议,以及最近推出的自动驾驶数据集和基于语义地图的3D对象检测的Kaggle竞赛,来帮助研究社区解决自动驾驶这个挑战. 自动驾驶数据集Level5链接:https://level5.lyft.com/dataset/ Kaggle竞赛链接:https://www.kaggle.com/c/3d-object-detection-for-autonomous-veh…

论文地址:面向基于深度学习的语音增强模型压缩 论文代码:没开源,鼓励大家去向作者要呀,作者是中国人,在语音增强领域 深耕多年 引用格式:Tan K, Wang D L. Towards model compression for deep learning based speech enhancem…

论文信息 论文标题:Structural and Semantic Contrastive Learning for Self-supervised Node Representation Learning论文作者: Kaize Ding .Yancheng Wang .Yingzhen Yang.…

目录 概 主要内容 代码 Yu Y., Chen J., Gao T. and Yu M. DAG-GNN: DAG structure learning with graph neural networks. In International Conference on Machine Learning (ICML), 2019. 概 有向无环图 + GNN + VAE. 主要内容 先前已经有工作(NOTEARS)讨论了如何处理线性SEM模型 \[X = A^TX + Z, \] \(A \i…

目录 引 主要内容 定理1 推论1 引理1 引理2 Safran I, Shamir O. Spurious Local Minima are Common in Two-Layer ReLU Neural Networks[J]. arXiv: Learning, 2017. @article{safran2017spurious, title={Spurious Local Minima are Common in Two-Layer ReLU Neural Networks}, autho…

1. 摘要 ReLU 相比 Tanh 能产生相同或者更好的性能,而且能产生真零的稀疏表示,非常适合自然就稀疏的数据. 采用 ReLU 后,在大量的有标签数据下,有没有无监督预训练模型取得的最好效果是一样的,这可以被看做是训练深层有监督网络的一个新的里程碑. 2. 背景 2.1. 神经元科学的观察 对脑能量消耗的研究表明,神经元以稀疏和分布的方式编码信息,同时活跃的神经元的百分比估计在 1% 到 4% 之间.这是信息表示丰富度和潜在能量消耗的一种平衡.但是,如果没有额外的正则化,比如 \(L_1\…

用中文记下这篇论文的大致意思,以防止忘了.好记性不如烂笔头! 摘要:最近的一些研究在研究社交网络或WWW.研究者都集中于研究网络的“小世界性”,“幂率分布特性”,“网络传递性”(聚类性吧).本文提出网络的另一个特性:社团结构——社团内部链接十分紧密,社团之间链接较为稀疏.我们分别模拟数据和真实数据测试了算法,效果很好.又应用在了两个不知道社团结构的数据集上,能帮助我们更好的理解数据. 首先,介绍了小世界效应,幂率分布,聚类系数.然后说,本文我们提出了社团结构这一网络属性.总结说我们提出了一种社区…

循环神经网络(RNN, Recurrent Neural Networks)介绍    这篇文章很多内容是参考:http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/,在这篇文章中,加入了一些新的内容与一些自己的理解.   循环神经网络(Recurrent Neural Networks,RNNs)已经在众多自然语言处理(Natural Language Proce…

A Beginner's Guide To Understanding Convolutional Neural Networks Introduction Convolutional neural networks. Sounds like a weird combination of biology and math with a little CS sprinkled in, but these networks have been some of the most influential…

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…

Hacker's guide to Neural Networks Hi there, I'm a CS PhD student at Stanford. I've worked on Deep Learning for a few years as part of my research and among several of my related pet projects is ConvNetJS - a Javascript library for training Neural Net…

Adit Deshpande CS Undergrad at UCLA ('19) Blog About A Beginner's Guide To Understanding Convolutional Neural Networks Introduction Convolutional neural networks. Sounds like a weird combination of biology and math with a little CS sprinkled in, but…

Hi there, I'm a CS PhD student at Stanford. I've worked on Deep Learning for a few years as part of my research and among several of my related pet projects is ConvNetJS - a Javascript library for training Neural Networks. Javascript allows one to ni…

论文地址:MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications 本文提出的模型叫MobileNet,主要用于移动和嵌入式视觉应用.该模型具有小巧.低延迟的特点.MobileNet在广泛的应用场景中具有有效性,包括物体检测,细粒度分类,人脸属性和大规模地理定位. MobileNet架构 深度可分解卷积(Depthwise Separable Convolution) MobileNet模…

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…