在本节中,我们将介绍AE2-Network,用于学习具有一组多视图样本 X=X(1),..,X(V) 的完整表示,其中 X(V)∈Rdv×n 是第v个视图的特征矩阵,其中V,n和 dv 分别是第V个视图的视图数量,样本数量和特征空间的维数。 2.1 方法简介 AE2-Nets(如图1所示)的关键目标是恢复一个完整的潜在空间,该空间可以很好地揭示多个视图中数据
TransVAE-DTA Transformer and variational autoencoder network for drug-target binding affinity prediction.pdf 1.3M· 百度网盘 摘要 背景和目的:最近的研究强调了计算机模拟药物靶点结合亲和力 (DTA) 预测在药物发现和药物再利用领域的重要性。然而,现有的 DTA 预测方法存在两大缺陷,阻碍了其进展。首先,虽然大多数...
function[n,k] = getAEWParameters(autoencoderType)%getAEWParameters Get the autoencoder parameters% [N,K] = getAEWParameters(autoencoderType) returns% the (N,K) parameter values for selected% autoencoder network% Copyright 2024 The MathWorks, Inc.switchautoencoderTypecase"2,2 Autoencoder"n = ...
生成对抗网络(Generative Adversarial Network,GAN)是一种无监督学习的深度学习模型,由Ian Goodfellow等人在2014年提出。GAN包含两个相互竞争的神经网络:生成器(Generator)和判别器(Discriminator)。生成器试图生成看起来像真实数据的假数据,而判别器则试图区分真实数据和生成数据。通过这种对抗过程,生成器能够生成非常逼真的...
https://github.com/ymirsky/KitNET-py 所发表的论文: Yisroel Mirsky, Tomer Doitshman, Yuval Elovici and Asaf Shabtai, “Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection” NDSS, 2018. 2018-11-05 09:56:13
Cai, "Network intrusion detection through stacking dilated convolutional autoencoders," Security and Com- munication Networks, vol. 2017, 2017.Yu, Y.; Long, J.; Cai, Z. Network intrusion detection through stacking dilated convolutional autoencoders. Secur. Commun. Netw. 2017, 2017, 4184196. ...
Network embedding has recently attracted lots of attention due to its wide applications on graph tasks such as link prediction, network reconstruction, node stabilization, and community stabilization, which aims to learn the low-dimensional representations of nodes with essential features. Most existing ...
To train an autoencoder network, we need to gather a large number of skill executions and represent them with DMPs θiDMP∈RdDMP,i=1,…,m. The following criterion function is then optimized (4)ζ⋆=argminζ1m∑i=1m‖θiDMP−FdecFencθiDMP‖2,where ζ⋆ are the autoencoder para...
(scRNA-seq) has enabled researchers to study gene expression at a cellular resolution. However, noise due to amplification and dropout may obstruct analyses, so scalable denoising methods for increasingly large but sparse scRNA-seq data are needed. We propose a deep count autoencoder network (DCA...
@article{jin2021adversarial, title={Adversarial Autoencoder Network for Hyperspectral Unmixing}, author={Jin, Qiwen and Ma, Yong and Fan, Fan and Huang, Jun and Mei, Xiaoguang and Ma, Jiayi}, journal={IEEE Transactions on Neural Networks and Learning Systems}, year={2021}, publisher={IEEE}...