Neural network architecturesYousefizadeh, HoomanZilouchian, Ali
Inception:network-in-network。神经网络的每一层都是另一个网络本身,可以用来在计算效率较高的情况下构建更深层次的网络 DenseNet:DenseNets connect the outputs of each layer to every subsequent layer resembling the connectivity pattern of fully-connected neural networks.每一层都接受它前面所有层的输出。 Neu...
Session-based recommendations with recurrent neural networks第一篇用rnn建模的论文,该论文用session中item的one-hot表示来训练排序损失,并预测item的点击概率,本文侧重点在于对点击item更丰富的特征建模。 Collaborative deep learning for recommender systems这篇论文论述了CNN如何用于item提取特征并结合CF用于推荐任务上。
Günter Klambauer, Thomas Unterthiner, Andreas Mayr, and Sepp Hochreiter. Self-normalizing neural networks. In Advances in Neural Information Processing Systems, pages 972–981, 2017. 这篇文章介绍了一种新的神经网络:self-normalizingneural network (SNN)。该网络独特之处在于,其使用了一个全新的激活函数...
CV:翻译并解读2019《A Survey of the Recent Architectures of Deep Convolutional Neural Networks》第一章~第三章 导读:人工智能领域,最新计算机视觉文章历史综述以及观察,深度卷积神经网络的最新架构综述。 原作者 Asifullah Khan1, 2*, Anabia Sohail1, 2, Umme Zahoora1, and Aqsa Saeed Qureshi1 ...
ConvolutionalNeuralNetworks(CNNs)areaspecialtypeofNeuralNetworks,whichhaveshownstate-of-the-artperformanceonvariouscompetitivebenchmarks.ThepowerfullearningabilityofdeepCNNislargelyduetotheuseofmultiplefeatureextractionstages(hiddenlayers)thatcanautomaticallylearnrepresentationsfromthedata.Availabilityofalargeamountofdata...
Types of neural network architectures [7 min] coursera Lecture from the course Neural Networks for Machine Learning, as taught by Geoffrey Hinton (University of Toronto) on Coursera in 2012.
Neural Network Architectures NeuralNetworkArchitectures AydınUlaş02December2004 ulasmehm@boun.edu.tr OutlineOfPresentation IntroductionNeuralNetworksNeuralNetworkArchitecturesConclusions Introduction Somenumbers…–Thehumanbraincontainsabout10billionnervecells(neurons)–Eachneuronisconnectedtothe...
[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 3、深度CNN结构演化史 3.1 1980年代末至1999年...
Convolutional neural network architectures for predicting DNA–protein binding CNN用于基因组学研究的最大优势之一是,它可以探测某一motif(指蛋白质分子具有特定功能的或者作为一个独立结构域一部分相近的二级结构聚合体)是否在指定序列窗口内,这种探测能力非常有利于motif的鉴定,进而有助于结合位点的分类...