LSTM Neural Network for Time Series Prediction Wed 21st Dec 2016 Neural Networks these days are the “go to” thing when talking about new fads in machine learning. As such, there’s a plethora of courses and tutorials out there on the basic vanilla neu…

LSTM NEURAL NETWORK FOR TIME SERIES PREDICTION Wed 21st Dec 2016   Neural Networks these days are the "go to" thing when talking about new fads in machine learning. As such, there's a plethora of courses and tutorials out there on the basic vani…

Problem: time series prediction The nonlinear autoregressive exogenous model: The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of a time series based upon its previous values as well as the current and past values…

Recurrent Neural Network 2016年07月01日  Deep learning  Deep learning 字数:24235   this blog from: http://jxgu.cc/blog/recent-advances-in-RNN.html    References Robert Dionne Neural Network Paper Notes Baisc Improvements 20170326 Learning Simpler Language…

摘要: 传统的评分预测只考虑到了文本信息,没有考虑到用户的信息,因为同一个词 在不同的用户表达中是不一样的.同样good 一词, 有人觉得5分是good 有人觉得4分是good.但是传统的文本向量表达无法区分.所以每个人都应该有一个属于自己的词向量. 传统的是word embedding的方式,这样处理,忽略了文档的生成者的特性. 因此本文讨论的是如何利用用户信息,来“修正”单词的特征表示. 作者提出了一套自己的表达词向量的方式,并不是用的word embedding.. 作者提出了将用户表示为…

KDD: Knowledge Discovery and Data Mining (KDD) Insititute: 复旦大学,中科大 Problem: time series prediction; modelling extreme events; overlook the existence of extreme events, which result in weak performance when applying them to real time series. 为什么研究ext…

How to Use Convolutional Neural Networks for Time Series Classification 2019-10-08 12:09:35 This blog is from: https://towardsdatascience.com/how-to-use-convolutional-neural-networks-for-time-series-classification-56b1b0a07a57 Introduction A large am…

yi作者:zhbzz2007 出处:http://www.cnblogs.com/zhbzz2007 欢迎转载,也请保留这段声明.谢谢! 本文翻译自 RECURRENT NEURAL NETWORK TUTORIAL, PART 4 – IMPLEMENTING A GRU/LSTM RNN WITH PYTHON AND THEANO . 本文的代码github地址 在此 .这是循环神经网络教程的第四部分,也是最后一个部分.之前的博文在此, RNN概述 利用Python,Theano实现RNN…

1.BP neural network optimized by PSO algorithm on Ammunition storage reliability prediction 文献简介文献来源:https://ieeexplore.ieee.org/document/8242856 文献级别:EI检索 摘要:Storage reliability of the ammunition dominates the efforts in achieving the mission reliab…

Handwritten digits recognition (0-9) Multi-class Logistic Regression 1. Vectorizing Logistic Regression (1) Vectorizing the cost function (2) Vectorizing the gradient (3) Vectorizing the regularized cost function (4) Vectorizing the regularized gradi…

0.引言 我们发现传统的(如前向网络等)非循环的NN都是假设样本之间无依赖关系(至少时间和顺序上是无依赖关系),而许多学习任务却都涉及到处理序列数据,如image captioning,speech synthesis,music generation是基于模型输出序列数据:如time series prediction,video analysis,musical information retrieval是基于模型输入需要序列数据:而如translating natural language…

0. Overview What is language models? A time series prediction problem. It assigns a probility to a sequence of words,and the total prob of all the sequence equal one. Many Natural Language Processing can be structured as (conditional) language modell…

转自:http://www.asimovinstitute.org/neural-network-zoo/ THE NEURAL NETWORK ZOO POSTED ON SEPTEMBER 14, 2016 BY FJODOR VAN VEEN   With new neural network architectures popping up every now and then, it's hard to keep track of them all. Knowing all the a…

作者:zhbzz2007 出处:http://www.cnblogs.com/zhbzz2007 欢迎转载,也请保留这段声明.谢谢! 本文翻译自 RECURRENT NEURAL NETWORKS TUTORIAL, PART 2 – IMPLEMENTING A RNN WITH PYTHON, NUMPY AND THEANO . github地址 在这篇博文中,我们将会使用Python从头开始实现一个循环神经网络,并且利用Theano(一个在GPU上执行操作的库)优化原始的实现.所有的代码…

Modern neuroscientists often discuss the brain as a type of computer. Neural networks aim to do the opposite: build a computer that functions like a brain. Of course, we only have a cursory understanding of the brain’s complex functions, but by creat…

Building your Recurrent Neural Network - Step by Step Welcome to Course 5's first assignment! In this assignment, you will implement your first Recurrent Neural Network in numpy. Recurrent Neural Networks (RNN) are very effective for Natural Language…

作者简介: 吴天龙  香侬科技researcher 公众号(suanfarensheng) 导言 图(graph)是一个非常常用的数据结构,现实世界中很多很多任务可以描述为图问题,比如社交网络,蛋白体结构,交通路网数据,以及很火的知识图谱等,甚至规则网格结构数据(如图像,视频等)也是图数据的一种特殊形式,因此图是一个很值得研究的领域. 针对graph的研究可以分为三类: 1.经典的graph算法,如生成树算法,最短路径算法,复杂一点的二分图匹配,费用流问题等等: 2.概率图模型,将条件概率表达为…

Building your Recurrent Neural Network - Step by Step Welcome to Course 5's first assignment! In this assignment, you will implement your first Recurrent Neural Network in numpy. Recurrent Neural Networks (RNN) are very effective for Natural Language…

论文地址:用于端到端语音增强的卷积递归神经网络 论文代码:https://github.com/aleXiehta/WaveCRN 引用格式:Hsieh T A, Wang H M, Lu X, et al. WaveCRN: An efficient convolutional recurrent neural network for end-to-end speech enhancement[J]. IEEE Signal Processing Letters, 2020, 27: 2149…

提出了模型和损失函数 论文名称:扩展卷积密集连接神经网络用于时域实时语音增强 论文代码:https://github.com/ashutosh620/DDAEC 引用:Pandey A, Wang D L. Densely connected neural network with dilated convolutions for real-time speech enhancement in the time domain[C]//ICASSP 2020-2020 IEEE Internati…

论文地址:FLGCNN:一种新颖的全卷积神经网络,用于基于话语的目标函数的端到端单耳语音增强 论文代码:https://github.com/LXP-Never/FLGCCRN(非官方复现) 引用格式:Zhu Y, Xu X, Ye Z. FLGCNN: A novel fully convolutional neural network for end-to-end monaural speech enhancement with utterance-based objective funct…

论文地址:TCNN:时域卷积神经网络用于实时语音增强 论文代码:https://github.com/LXP-Never/TCNN(非官方复现) 引用格式:Pandey A, Wang D L. TCNN: Temporal convolutional neural network for real-time speech enhancement in the time domain[C]//ICASSP 2019-2019 IEEE International Conference on Ac…

作者:zhbzz2007 出处:http://www.cnblogs.com/zhbzz2007 欢迎转载,也请保留这段声明.谢谢! 本文翻译自 RECURRENT NEURAL NETWORKS TUTORIAL, PART 1 – INTRODUCTION TO RNNS . Recurrent Neural Networks(RNNS) ,循环神经网络,是一个流行的模型,已经在许多NLP任务上显示出巨大的潜力.尽管它最近很流行,但是我发现能够解释RNN如何工作,以及如何实现RNN的资料很少…

神经网络的实践笔记 link: http://peterroelants.github.io/posts/neural_network_implementation_part01/ 1. 生成训练数据 import numpy as np import matplotlib.pyplot as plt # 神经网络中有关# 矩阵的运算我们采用NumPy来构建,# 画图使用Matplotlib来构建. # Part 1, create training data # Define the vect…

白翔的CRNN论文阅读 1.  论文题目 Xiang Bai--[PAMI2017]An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition 2.  论文思路和方法 1)  问题范围: 单词识别 2)  CNN层:使用标准CNN提取图像特征,利用Map-to-Sequence表示成特征向量: 3)  RNN层:使…

Reference:   Alex Graves的[Supervised Sequence Labelling with RecurrentNeural Networks] Alex是RNN最著名变种,LSTM发明者Jürgen Schmidhuber的高徒,现加入University of Toronto,拜师Hinton. 统计语言模型与序列学习 1.1 基于频数统计的语言模型 NLP领域最著名的语言模型莫过于N-Gram. 它基于马尔可夫假设,当然,这是一个2-Gram(Bi-Gram)模…

Progressive Neural Network  Google DeepMind 摘要:学习去解决任务的复杂序列 --- 结合 transfer (迁移),并且避免 catastrophic forgetting (灾难性遗忘) --- 对于达到 human-level intelligence 仍然是一个关键性的难题.本文提出的 progressive networks approach 朝这个方向迈了一大步:他们对 forgetting 免疫,并且可以结合 prior knowledg…

A Neural Network in 11 lines of Python A bare bones neural network implementation to describe the inner workings of backpropagation. Posted by iamtrask on July 12, 2015 Summary: I learn best with toy code that I can play with. This tutorial teaches b…

作者:zhbzz2007 出处:http://www.cnblogs.com/zhbzz2007 欢迎转载,也请保留这段声明.谢谢! 这是RNN教程的第三部分. 在前面的教程中,我们从头实现了一个循环神经网络,但是并没有涉及随时间反向传播(BPTT)算法如何计算梯度的细节.在这部分,我们将会简要介绍BPTT并解释它和传统的反向传播有何区别.我们也会尝试着理解梯度消失问题,这也是LSTM和GRU(目前NLP及其它领域中最为流行和有用的模型)得以发展的原因.梯度消失问题最早是由 Sepp Hochr…

为什么使用序列模型(sequence model)?标准的全连接神经网络(fully connected neural network)处理序列会有两个问题:1)全连接神经网络输入层和输出层长度固定,而不同序列的输入.输出可能有不同的长度,选择最大长度并对短序列进行填充(pad)不是一种很好的方式:2)全连接神经网络同一层的节点之间是无连接的,当需要用到序列之前时刻的信息时,全连接神经网络无法办到,一个序列的不同位置之间无法共享特征.而循环神经网络(Recurrent Neural Networ…