Vamb is a family of metagenomic binners which feeds kmer composition and abundance into a variational autoencoder and clusters the embedding to form bins. Its binners perform excellently with multiple samples, and pretty good on single-sample data. ...
Variational Autoencoder with Arbitrary Conditioning (VAEAC) is a neural probabilistic model based on variational autoencoder that can be conditioned on an arbitrary subset of observed features and then sample the remaining features.For more detail, see the following paper: Oleg Ivanov, Michael Figurn...
VAE(Variational Autoencoder)假设样本x经由隐变量z生成,分为两步:1)从先验分布pθ(z)中采样隐变量z;2)从条件分布pθ(x|z)中采样样本x。生成模型的学习目标是使得数据的对数似然最大,即: θ∗=argmaxθ∑i=1nlogpθ(x(i)) 一个直观的pθ(x)的展开式为: pθ(x)=∫pθ(x,z)dz=∫p...
作者使用Caffe架构实现了上述想法,其源代码位于:GitHub - cdoersch/vae_tutorial: Caffe code to accompany my Tutorial on Variational Autoencoders 4.1 MNIST变分自编码器 为了展示所述框架的分布学习能力,让我们在MNIST上从头开始训练一个变分自编码器。为了证明该框架不严重依赖初始化或者网络结构,我们不使用现存的...
转自:http://kvfrans.com/variational-autoencoders-explained/ 下面是VAE的直观解释,不需要太多的数学知识。 什么是 变分自动编码器? 为了理解VAE,我们首先从最简单的网络说起,然后再一步一步添加额外的部分。 一个描述神经网络的常见方法是近似一些我们想建模的函数。然而神经网络也可以被看做是携带信息的数据结构...
https://github.com/vaxin/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/variational_autoencoder.py 里面的每一步,都有配合本文章的对照解释。 5. 延伸思考 之所以关注VAE,是从文献[4]引发的,由于视觉早期的概念形成对于之后的视觉认知起了十分关键的作用,我们有理由相信,在神经网络训练时,利用这种递...
Variational Autoencoder Variational Recurent Neural Network Generative models in SNN 脉冲GAN(Kotariya和Ganguly 2021)使用两层SNN构造生成器和鉴别器来训练GAN;生成的图像的质量低。其中一个原因是,初次脉冲时间编码(time-to-first spike encoding)不能在脉冲序列的中间抓取整个图像。此外,由于SNN的学习是不稳定的...
Variational autoencoders (VAEs) play an important role in high-dimensional data generation based on their ability to fuse the stochastic data representation with the power of recent deep learning techniques. The main advantages of these types of generators lie in their ability to encode the informa...
We propose a method (https://github.com/lihenryhfl/vdae) that combines these approaches into a generative model that inherits the asymptotic guarantees of diffusion maps while preserving the scalability of deep models. We prove approximation theoretic results for the dimension dependence of our ...
Thus, we build the Fully Spiking Variational Autoencoder where all modules are constructed with SNN. To the best of our knowledge, we are the first to build a VAE only with SNN layers. We experimented with several datasets, and confirmed that it can generate images with the same or better...