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
First, a new model of Variational Autoencoders (VAEs) with a Gaussian Random Field (GRF) prior is presented. This offers a relevant way to model images with strong spatial correlations. Second, the VAE-GRF is used in the context of Anomaly Detection (AD). More precisely, we address the...
Fig. 3: Performance of variational autoencoder models. Comparison of TopoGNN, GNN, and Topo in terms of polymer graph reconstruction, \(\langle {R}_{{{\rm{g}}}^{2}\rangle\) regression, and topology classification. BACC represents balanced accuracy, R2 is the coefficient of determination...
Xie et al.9 has proposed a model for crystal prediction by a combination between variational autoencoder (VAE)25 and the denoising diffusion model, called crystal diffusion VAE (CDVAE). The model employs the score matching approach with (annealed) Langevin dynamics to generate new crystal ...
In a classic standard autoencoder, the intermediate latent space signifies the input data as discrete points. It will recreate the original data when the appropriate input feeds into the inference engine. But it will fail when anomalous data is input, making it a good anomaly detector....
Autonomous Intelligent Systems https://doi.org/10.1007/s43684-024-00065-x (2024) 4:8 Autonomous Intelligent Systems ORIGINAL ARTICLE Open Access Variational autoencoder-based techniques for a streamlined cross-topology modeling and optimization workflow in electrical drives Marius Benkert1* , Michael ...
This paper proposes Dirichlet Variational Autoencoder (DirVAE) using a Dirichlet prior. To infer the parameters of DirVAE, we utilize the stochastic gradient method by approximating the inverse cumulative distribution function of the Gamma distribution, which is a component of the Dirichlet distribution...
Understanding Variational Autoencoders (VAEs) 为何不能用AE的decoder来直接生成数据? 因为这里的latent space的regularity无法保证 右边给出的例子,AE只是保证training过程中的cases的这些离散点,会导致严重的overfitting,你选中其他点的时候,不知道会发生什么,因为对于latent space之前是没有任何约束的 ...
A strong integration method we mentioned in the introduction is scGLUE9, which utilizes a combined autoencoder and graph model. To facilitate a comparison between scGLUE and JAMIE, we also ran both methods on Chen et al.21, which was utilized in Cao et al.9. We found that JAMIE (LTA ...
The goal of the autoencoder is to recreate the original data; therefore, the metric used com- pares the input data with the output data, calculating the difference between them. This metric is called reconstruction loss (1), and in most cases mean squared error is used: loss = ||x −...