We will start with a general introduction to autoencoders, and we will discuss the role of the activation function in the output layer and the loss function. We will then discuss what the reconstruction error is. Finally, we will look at typical applications as dimensionality reduction, ...
Fig. 5. General autoencoder — visualization of a latent space and its transformations. View article Journal 2024, Journal of Energy StorageRam Machlev Chapter Smart energy and electric power system: current trends and new intelligent perspectives and introduction to AI and power system 2.8.1 Auto...
3.1 Denoising autoencoder The proposal of the denoising autoencoder (DAE) was inspired by human behavior. Humans can accurately identify a target even when the image is partially obscured. Similarly, if the data reconstructed using data with noise is almost identical to clean data, this encoder ...
1 Introduction 我们知道,CNNs、RNNs以及 autoencoders 等深度学习方法,可以取代手工的特征提取,有效地捕获欧氏数据的隐含特征。但现实生活中,数据更普遍的形式是可以被构建为图的非欧数据。例如,化学分子结构、知识图谱、电子商务等。 由于图可能是不规则的,节点大小、邻居数量不同,从而传统深度学习难以应用于图域。...
Autoencoders are a type of generative model used for unsupervised learning. Autoencoders learn some latent representation of the image and use that to reconstruct the image. What is this “latent representation”? It is another fancy term for hidden features of the image. Autoencoders, through...
By interpreting a communications system as an autoencoder, we develop a fundamental new way to think about communications system design as an end-to-end reconstruction task that seeks to jointly optimize transmitter and receiver components in a single process. We show how this idea can be ...
Recent advances in spatially resolved transcriptomics have enabled comprehensive measurements of gene expression patterns while retaining the spatial context of the tissue microenvironment. Deciphering the spatial context of spots in a tissue needs to us
An Introduction to Computational Networks and the Computational Network Toolkit Amit Agarwal, Eldar Akchurin, Chris Basoglu, Guoguo Chen, Scott Cyphers, Jasha Droppo, Adam Eversole, Brian Guenter, Mark Hillebrand, Xuedong Huang, Zhiheng Huang, Vladimir Ivanov, Alexey Kamenev, Philipp Kranen, Oleksii...
This autoencoder-based generative model is an individual component that separates from TacticAI’s predictive systems. All three systems share the encoder architecture (without sharing parameters), but use different decoders (see the “Methods” section). At inference time, we can instead feed in ...
End-to-end neural audio codecs rely on data-driven methods to learn efficient audio representations, instead of relying on handcrafted signal processing components.Autoencoder networks with quantization of hidden features were applied to speech coding early on [37]. More recently, a more sophisticate...