VARIATIONAL RECURRENT AUTO-ENCODERS 详解 摘要 在本文中,我们提出了一个结合了RNN和SGVB优势的模型:变分自动编码器(VRAE)。 这种模型可用于对时间序列数据进行有效的大规模无监督学习,将时间序列数据映射到潜在向量表示。 该模型是生成模型,因此可以从隐藏空间的样本生成数据。 这项工作的一个重要贡献是该模型可以利...
Recurrent neural networkVariational autoencoderThe progression of neurodegenerative diseases, such as Alzheimer's Disease, is the result of complex mechanisms interacting across multiple spatial and temporal scales. Understanding and predicting the longitudinal course of the disease requires harnessing the ...
In this paper we propose a model that combines the strengths of RNNs and SGVB: the Variational Recurrent Auto-Encoder (VRAE). Such a model can be used for efficient, large scale unsupervised learning on time series data, mapping the time series data to a
VARIATIONAL RECURRENT AUTO-ENCODERS 详解 机器学习神经网络深度学习人工智能编程算法 在本文中,我们提出了一个结合了RNN和SGVB优势的模型:变分自动编码器(VRAE)。 这种模型可用于对时间序列数据进行有效的大规模无监督学习,将时间序列数据映射到潜在向量表示。 该模型是生成模型,因此可以从隐藏空间的样本生成数据。 这...
Variational Recurrent Auto-encoders (VRAE) VRAE is a feature-based timeseries clustering algorithm, since raw-data based approach suffers fromcurse of dimensionalityand is sensitive to noisy input data. The middle bottleneck layer will serve as the feature representation for the entire input timeser...
This section presents a new stochastic learning machine for speech separation based on the variational recurrent neural network (VRNN) (Chung et al., 2015, Chien and Kuo, 2017). This VRNN is constructed from the perspectives of generative stochastic network and variational autoencoder. The basic...
5. Stochastic Recurrent Neural Networks (Stochastic RNNs)6. Recurrent Variational Autoencoder (RVAE)7. 解耦序列自动编码器 深度生成模型是一种概率模型的统称，广泛应用于数据处理领域。此类模型结合了深度神经网络与传统生成概率模型，分为显式表达概率密度与直接生成数据的两派。深度动态贝叶斯网络从...
As a powerful text generation model, the Variational AutoEncoder(VAE) has attracted more and more attention. However, in the process of optimization, the variational auto-encoder tends to ignore the potential variables and degenerates into an auto-encode
It has been previously observed that training Variational Recurrent Autoencoders (VRAE) for text generation suffers from serious uninformative latent variables problems. The model would collapse into a plain language model that totally ignores the latent variables and can only generate repeating and dull...
In this paper we propose a model that combines the strengths of RNNs and SGVB: the Variational Recurrent Auto-Encoder (VRAE). Such a model can be used for efficient, large scale unsupervised learning on time series data, mapping the time series data to a latent vector representation. The ...