ABSTRACT——引入了一个新的基于卷积神经网络的自动编码器架构,称为SEHAE(语音增强分层自动编码器),其中潜伏表示被分解成对应于不同尺度的几个部分。该模型由三个功能不同的部件组成。首先,一堆编码器生成一组潜伏向量,这些潜伏向量包含来自一个越来越大的感受野的信息。第二,解码器从学习到的初始向量开始,以分阶段...
其实还有更狂的,我还看过有拿 Tree Structure,当做 Embedding,就一段文字把它变成 Tree Structure,再用 Tree Structure 还原一段文字, 好 我把 Reference 列在这边给大家参考 More Applications 接下来啊,还有 Aauto-Encoder 更多的应用,Aauto-Encoder 还可以拿来做些什么事情呢,举例来说,我们刚才用的都是 Encode...
Fig. 1. Autoencoder structure. An input x is mapped to obtain a reconstruction r by means of using a latent representation h. f is a function that encodes or maps x to h and g is a function that decodes, that is, maps h to r. Show moreView article Journal 2021, Expert Systems ...
In this paper, we consider both the global and local structure of the features in the autoencoders by the low-rank property and Laplace operator, respectively. The low-rank property can make the learned features favoring similarity extraction by self-representation and the Laplace operator can ...
TensorFlow Fold is a library for creatingTensorFlowmodels that consume structured data, where the structure of the computation graph depends on the structure of the input data. For example,this modelimplementsTreeLSTMsfor sentiment analysis on parse trees of arbitrary shape/size/depth. ...
In the previous section, I established the statistical motivation for a variational autoencoder structure. In this section, I'll provide the practical implementation details for building such a model yourself. Rather than directly outputting values for the latent state as we would in a standard auto...
structure has kernel sizes that are in the reverse of the encoder order and uses Transposed Convolution Layers. The output from the encoder layers is concatenated with the previous layers before passing to layers x7 to x9. For every Conv2D(Transpose) layer the parameters shown are kernel size,...
S4). For example, STAGATE depicted a clear “cord-like” structure as well as an “arrow-like” structure in the hippocampal region and identified four spatial domains of it. This result is consistent with the annotation of hippocampus structures from the Allen Reference Atlas24 (Fig. 3b). ...
we introduced a graph convolutional autoencoder that integrates both the gene expression of a cell and that of its neighbors. Our graph-based autoencoder structure decodes both a cell’s gene expression profile as well as its adjacencies. Unlike when using other graph convolutional methods43,46...
directly be assessed by performing supervised learning experiments with unsupervised pre-training, what has remained until recently rather unclear is the interpretation of these algorithms in the context of pure unsupervised learning, as devices to capture the salient structure of the input data ...