part 4: topologically structured networks incorporating structure in networks of point neurons 如果我们使用神经元的生物学详细模型,那么很容易理解和实现拓扑的概念,因为我们已经有树突状乔木,轴突等,它们是神经系统内连接的物理先决条件. 但是,我们仍然可以通过使用点神经元网络来获得一定程度的特异性. 无论是在拓扑结构还是日常意义上,结构都可以被看作是一组规则,用于规定对象的位置以及它们之间的关系. 在点神…

Pixel Recurrent Neural Networks 目前主要在用的文档存放: https://www.yuque.com/lart/papers/prnn github存档: https://github.com/lartpang/Machine-Deep-Learning 介绍 Google DeepMind generative model 引言 生成图像建模是无监督学习中的核心问题. 在无监督学习中,自然图像的分布建模是一个具有里程碑意义的问题.此任务需要一个图像模型,它同时具…

really-awesome-gan A list of papers and other resources on General Adversarial (Neural) Networks. This site is maintained by Holger Caesar. To complement or correct it, please contact me at holger-at-it-caesar.com or visit it-caesar.com. Also checkou…

E_GEOMETRY_AMBIGUOUSPARTTYPE - Static variable in interface com.esri.arcgis.geometry.esriGeometryError (0x8004024c) The operation would result in the creation of a new part, but the type of part to be created was ambiguous. E_GEOMETRY_AUTHORITY_TOO_L…

Text Classification For purpose of word embedding extrinsic evaluation, especially downstream task. Some concepts are informed from 复旦大学NLP组 Statistical-Based Method Logistic Regression Statistics perspective based text classification described as fo…

STA的主要工作是计算电路网络的延时,如今的电路网络还是由CMOS cell和net组成的,所以STA所要计算的延时仍是电容的充放电时间.等量子计算机普及的时候,如今的这一套理论都将随着科技的进步被丢到故纸堆里.在量子计算机君临之前,如今的天下还是CMOS的,所以要搞STA,首先需要明白如何计算CMOS cell delay跟net delay. STA所有的行为都可以概括为建模和解方程,在STA眼里电路网络就是一张RC网络.cell被模拟成输入电容.输出电容.上拉电阻和下拉电阻.输入电容为前一…

part 3: connecting networks with synapses parameterising synapse models NEST提供了各种不同的突触模型. 您可以使用命令nest.Models(mtype ='synapses')查看可用模型,该命令仅从所有可用模型列表中选取突触模型. Synapse模型可以类似于神经元模型进行参数化. 您可以使用GetDefaults(模型)发现默认参数设置,并使用SetDefaults(模型,参数)设置它们: nest.SetDefa…

neurons and simple neural networks pynest – nest模拟器的界面 神经模拟工具(NEST:www.nest-initiative.org)专为仿真点神经元的大型异构网络而设计. 它是根据GPL许可证发布的开源软件. 该模拟器带有Python的接口[4]. 图1说明了用户的模拟脚本(mysimulation.py)和NEST模拟器之间的交互. [2]包含该接口实现的技术详细描述,本文的部分内容均基于此参考. 仿真内核使用C ++编写,以获得最高性能的仿真…

Action Recognition: 行为识别,视频分类,数据集为剪辑过的动作视频 Temporal Action Detection: 从未剪辑的视频,定位动作发生的区间,起始帧和终止帧并预测类别 难点 1: 边界不明确(助跑跳远,上篮,高尔夫挥杆) 2: 如何利用时序信息 3: 时序跨度大(Activitynet:1s — 200s) 上图为模型框架,用temporal actionness grouping算法提取proposal后进行上下文信息的金字塔池化,后接两个级联分类器分别是完整…

colah's blog Blog About Contact Neural Networks, Manifolds, and Topology Posted on April 6, 2014 topology, neural networks, deep learning, manifold hypothesis Recently, there’s been a great deal of excitement and interest in deep neural networks beca…

Deep Metric Learning via Lifted Structured Feature Embedding CVPR 2016 摘要:本文提出一种距离度量的方法,充分的发挥 training batches 的优势,by lifting the vector of pairwise distances within the batch to the matrix of pairwise distances. 刚开始看这个摘要,有点懵逼,不怕,后面会知道这段英文是啥意思的. 引言部分…

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…

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…

转载请注明出处: http://www.cnblogs.com/fraud/          ——by fraud Series-Parallel Networks Input: standard input Output:  standard output Time Limit: 5 seconds Memory Limit: 32 MB In this problem you are expected to count two-terminal series-parallel networ…

ARVE:车辆到边缘网中的增强现实应用 本文为SIGCOMM 2018 Workshop (Mobile Edge Communications, MECOMM)论文. 笔者翻译了该论文.由于时间仓促,且笔者英文能力有限,错误之处在所难免:欢迎读者批评指正. 本文及翻译版本仅用于学习使用.如果有任何不当,请联系笔者删除. 本文作者包含4位,Pengyuan Zhou@University of Helsinki, Finland:Wenxiao Zhang@Hong Kong Universit…

A Structured Self-Attentive Sentence Embedding ICLR 2017 2018-08-19 14:07:29 Paper:https://arxiv.org/pdf/1703.03130.pdf Code(PyTorch): https://github.com/kaushalshetty/Structured-Self-Attention Video Tutorial (Youtube): Ivan Bilan: Understanding and…

中文版:https://zhuanlan.zhihu.com/p/27440393 原文版:https://www.oreilly.com/learning/generative-adversarial-networks-for-beginners “熟练tensorflow后,需研读实践的文章” 自从两年前蒙特利尔大学的Ian Goodfellow等人提出生成式对抗网络(Generative Adversarial Networks,GAN)的概念以来,GAN呈现出井喷式发展. // 竟然是G…

Diffusion-Convolutional Neural Networks (传播-卷积神经网络)2018-04-09 21:59:02 1. Abstract: 我们提出传播-卷积神经网络(DCNNs),一种处理 graph-structured data 的新模型.随着 DCNNs 的介绍,我们展示如何从 graph structured data 中学习基于传播的表示(diffusion-based representations),然后作为节点分类的有效基础.DCNNs 拥有多个有趣…

ResNet, AlexNet, VGG, Inception: Understanding various architectures of Convolutional Networks by KOUSTUBH        this blog from: http://cv-tricks.com/cnn/understand-resnet-alexnet-vgg-inception/ Convolutional neural networks are fantastic for visual…

Logistic Regression with a Neural Network mindset Welcome to the first (required) programming exercise of the deep learning specialization. In this notebook you will build your first image recognition algorithm. You will build a cat classifier that r…

1.What does the analogy “AI is the new electricity” refer to?  (B) A. Through the “smart grid”, AI is delivering a new wave of electricity. B. Similar to electricity starting about 100 years ago, AI is transforming multiple industries. C. AI is power…

Huang, Po-Sen, et al. "Learning deep structured semantic models for web search using clickthrough data." Proceedings of the 22nd ACM international conference on Conference on information & knowledge management. ACM, 2013. 该网络把两个不同的输入映射到相同的语义…

Coursera课程<Neural Networks and Deep Learning> deeplearning.ai Week1 Introduction to deep learning What is a Neural Network? 让我们从一个房价预测的例子开始讲起. 假设你有一个数据集,它包含了六栋房子的信息.所以,你知道房屋的面积是多少平方英尺或者平方米,并且知道房屋价格.这时,你想要拟合一个根据房屋面积预测房价的函数. 如果使用线性回归进行拟合,那么可以拟合出一条直线.但…

http://karpathy.github.io/2015/05/21/rnn-effectiveness/ There’s something magical about Recurrent Neural Networks (RNNs). I still remember when I trained my first recurrent network for Image Captioning. Within a few dozen minutes of training my first…

Neural Networks and Deep Learning This is the first course of the deep learning specialization at Coursera which is moderated by moderated by DeepLearning.ai. The course is taught by Andrew Ng. Introduction to deep learning Be able to explain the maj…

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…

About this Course If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "s…

Research Guide: Pruning Techniques for Neural Networks 2019-11-15 20:16:54 Original: https://heartbeat.fritz.ai/research-guide-pruning-techniques-for-neural-networks-d9b8440ab10d Pruning is a technique in deep learning that aids in the development of…

译自:http://sebastianruder.com/multi-task/ 1. 前言 在机器学习中,我们通常关心优化某一特定指标,不管这个指标是一个标准值,还是企业KPI.为了达到这个目标,我们训练单一模型或多个模型集合来完成指定得任务.然后,我们通过精细调参,来改进模型直至性能不再提升.尽管这样做可以针对一个任务得到一个可接受得性能,但是我们可能忽略了一些信息,这些信息有助于在我们关心的指标上做得更好.具体来说,这些信息就是相关任务的监督数据.通过在相关任务间共享表示信息,我们的模型在…

Fully Convolutional Networks for Semantic Segmentation 译文 Abstract   Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed…