目录 引 主要内容 定理1 Claim 1 Claim 2 定理2 证明 定理1的证明 Claim 1 的证明 Kronecker product (克罗内克积) Theorem 2 的证明 代码 Arora S, Cohen N, Hazan E, et al. On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization[J]. arXiv: Learning, 2018. 引 我很喜欢…

Initialization of deep networks 24 Feb 2015Gustav Larsson As we all know, the solution to a non-convex optimization algorithm (like stochastic gradient descent) depends on the initial values of the parameters. This post is about choosing initializati…

郑重声明:原文参见标题,如有侵权,请联系作者,将会撤销发布! Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS) 2017, Fort Lauderdale, Florida, USA. JMLR: W&CP volume 54. Copyright 2017 by the author(s). Abstract 现代移动设备可以访问大量适合模型学…

前言 1.理论知识:UFLDL教程.Deep learning:十六(deep networks) 2.实验环境:win7, matlab2015b,16G内存,2T硬盘 3.实验内容:Exercise: Implement deep networks for digit classification.利用深度网络完成MNIST手写数字数据库中手写数字的识别.即:用6万个已标注数据(即:6万张28*28的图像块(patches)),作为训练数据集,然后把它输入到栈式自编码器中,它的第一层自编码器…

(一)Highway Networks 与 Deep Networks 的关系 理论实践表明神经网络的深度是至关重要的,深层神经网络在很多方面都已经取得了很好的效果,例如,在1000-class ImageNet数据集上的图像分类任务通过利用深层神经网络把准确率从84%提高到了95%,然而,在训练深层神经网络的时候却是非常困难的,神经网络的层数越多,存在的问题也就越多(例如大家熟知的梯度消失.梯度爆炸问题,下文会详细讲解).训练起来也就是愈加困难,这是一个公认的难题. 2015年由Rupesh…

SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks 2019-04-02 12:44:36 Paper:https://arxiv.org/pdf/1812.11703.pdf Project:https://lb1100.github.io/SiamRPN++ 1. Background and Motivation: 与 CVPR 2019 的另一篇文章 Deeper and Wider Siames…

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks ICML 2017 Paper:https://arxiv.org/pdf/1703.03400.pdf Code for the regression and supervised experiments:https://github.com/cbfinn/maml Code for the RL experiments:https://github.com/cb…

Exercise: Implement deep networks for digit classification 习题链接:Exercise: Implement deep networks for digit classification stackedAEPredict.m function [pred] = stackedAEPredict(theta, inputSize, hiddenSize, numClasses, netconfig, data) % stackedAEPre…

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…

深度学习中潜藏的稀疏表达 Deep Networks for Image Super-Resolution with Sparse Prior http://www.ifp.illinois.edu/~dingliu2/iccv15/ 浅谈深度学习中潜藏的稀疏表达 | 统计之都https://cosx.org/2016/06/discussion-of-sparse-coding-in-deep-learning 浅谈深度学习中潜藏的稀疏表达 - 菜鸡一枚 - 博客园 http://www.cn…

Rupesh Kumar SrivastavaKlaus Greff ̈J urgenSchmidhuberThe Swiss AI Lab IDSIA / USI / SUPSI{rupesh, klaus, juergen}@idsia.ch AbstractTheoretical and empirical evidence indicates that the depth of neural networksis crucial for their success. However, t…

Overview In the previous sections, you constructed a 3-layer neural network comprising an input, hidden and output layer. While fairly effective for MNIST, this 3-layer model is a fairly shallow network; by this, we mean that the features (hidden lay…

目录 概 主要内容 代码 Wang Y., Huang G., Song S., Pan X., Xia Y. and Wu C. Regularizing Deep Networks with Semantic Data Augmentation. TPAMI. 概 通过data augments来对数据进行扩充, 可以有效提高网络的泛化性. 但是这些transformers通常只有一些旋转, 剪切等较为简单的变换, 想要施加更为复杂的语义不变变换(如切换背景), 可能就需要GAN等引入额外的…

目录 Grad-CAM:Visual Explanations from Deep Networks via Gradient-based Localization 1.Abstract 2.Introduction 3.Approach 4.Evaluating Localization 4.1. Weakly-supervised Localization 4.2 Weakly-supervised Segmentation 5.Evaluating Visualizations 5.1 E…

B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, "Communication-Efficient Learning of Deep Networks from Decentralized Data," in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Apr. 2017…

神经网络已经在很多场景下表现出了很好的识别能力,但是缺乏解释性一直所为人诟病.<Grad-CAM:Visual Explanations from Deep Networks via Gradient-based Localization>这篇论文基于梯度为其可解释性做了一些工作,它可以显著描述哪块图片区域对识别起了至关重要的作用,以热度图的方式可视化神经网络的注意力.本博客主要是基于pytorch的简单工程复现.原文见这里,本代码基于这里. 1 import torch 2 import t…

I. 背景介绍 1. 学习曲线(Learning Curve) 我们都知道在手工调试模型的参数的时候,我们并不会每次都等到模型迭代完后再修改超参数,而是待模型训练了一定的epoch次数后,通过观察学习曲线(learning curve, lc) 来判断是否有必要继续训练下去.那什么是学习曲线呢?主要分为两类: 1.模型性能是训练时间或者迭代次数的函数:performance=f(time) 或 performance=f(epoch).这个也就是我们常用到的方法,即横轴记录训练时间(或迭代次数)…

郑重声明:原文参见标题,如有侵权,请联系作者,将会撤销发布! arXiv:1812.06127v3 [cs.LG] 11 Jul 2019 目录: Abstract 1 Introduction 2 Related Work 3 Federated Optimization: Algorithms 3.1 Federated Averaging (FedAvg) 3.2 Proposed Framework: FedProx 4 FedProx: Convergence Analysis 4.1…

本文是谷歌发表在NeurIPS 2012上的一篇论文,主要讨论了在几万个CPU节点上训练大规模深度网络的问题,并提出了一个名为DistBelief的软件框架.在该框架下实现了两种大规模分布式训练算法:Downpour SGD和Sandblaster L-BFGS,这两种算法都增加了深度网络训练的规模和速度. Introduction 最近几年,深度学习在语音识别.图像识别以及自然语言处理等领域大放异彩.就训练样本的数量和模型参数的数量而言,增加深度学习的规模可以极大地提高最终模型的效果.GPU的…

目标: 怎么训练很深的神经网络 然而过深的神经网络会造成各种问题,梯度消失之类的,导致很难训练 作者利用了类似LSTM的方法,通过增加gate来控制transform前和transform后的数据的比例,称为Highway network 至于为什么会有效...大概和LSTM会有效的原因一样吧. 方法: 首先是普通的神经网络,每一层H从输入x映射到输出y,H通常包含一个仿射变换和一个非线性变换,如下 在这个基础上,highway network添加了两个gate 1)T:trasform gat…

In this section, we describe how you can fine-tune and further improve the learned features using labeled data. When you have a large amount of labeled training data, this can significantly improve your classifier's performance. In self-taught learni…

摘要:我们提出了一种不依赖模型的元学习算法,它与任何梯度下降训练的模型兼容,适用于各种不同的学习问题,包括分类.回归和强化学习.元学习的目标是在各种学习任务上训练一个模型,这样它只需要少量的训练样本就可以解决新的学习任务.在我们的方法中,模型的参数被显式地训练,使得少量的梯度步骤和少量的来自新任务的训练数据能够在该任务上产生良好的泛化性能.实际上,我们的方法训练模型易于微调.结果表明,该方法在两个few shot图像分类基准上都取得了最新的性能,在少镜头回归上取得了良好的效果,并加速了基于神经网…

Rupesh Kumar Srivastava (邮箱:RUPESH@IDSIA.CH)Klaus Greff (邮箱:KLAUS@IDSIA.CH)J¨ urgen Schmidhuber (邮箱:JUERGEN@IDSIA.CH)The Swiss AI Lab IDSIA(瑞士AI实验室IDSIA)Istituto Dalle Molle di Studi sull’Intelligenza Artificiale(IDSIA:institute of studies on intellig…

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…

Train/Dev/Test set Bias/Variance Regularization  有下面一些regularization的方法. L2 regularation drop out data augmentation(翻转图片得到一个新的example), early stopping(画出J_train 和J_dev 对应于iteration的图像) L2 regularization: Forbenius Norm. 上面这张图提到了weight decay 的概念 Weigh…

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…

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…

CNN综述文章 的翻译 [2019 CVPR] A Survey of the Recent Architectures of Deep Convolutional Neural Networks 翻译 综述深度卷积神经网络架构:从基本组件到结构创新 目录 摘要    1.引言    2.CNN基本组件        2.1 卷积层        2.2 池化层        2.3 激活函数        2.4 批次归一化        2.5 Dropout        2.6 全连接层…

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…

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…