Visual Studio Magazine

Autoencoder Anomaly Detection Using PyTorch

Dr. James McCaffrey of Microsoft Research provides full code and step-by-step examples of anomaly detection, used to find items in a dataset that are different from the majority for tasks like ...
C&EN

MODAPro: Explainable Heterogeneous Networks with Variational Graph Autoencoder for Mining Disease-Specific Functional Molecules and Pathways from Omics Data

State Key Laboratory of Medical Proteomics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, P. R. China University of Chinese Academy of Sciences, Beijing 100049, P.
C&EN

Soft Measurement Modeling Method Based on Quality-Related Mixed Variational Autoencoder Regression

Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems, School of Engineering, Huzhou University, Huzhou 313000, PR China Zhejiang Key Laboratory of Industrial Solid ...
IEEE

A Sensor Data Imputation Method Based on Variational Autoencoder Generative Adversarial Networks

Abstract: Data from industrial processes often experiences loss of critical information due to sensor or transmission equipment failures. This results in significant deviations in subsequent data ...
IEEE

Automatically Inverse Design of FSSs by Conditional Variational Autoencoder and Equivalent Circuit Models with Large Language Models

Abstract: Recently, deep learning has attracted intensive attentions in electromagnetic society, especially for inverse problems. In this work, we propose two language-guided automatic design ...
GitHub

HiSTaR : identifying spatial domains in spatial transcriptomics data with Hierarchical Decoupled Variational Autoencoder

we propose a Hierarchical ST variational autoencoder (HiSTaR) to extract multi-level latent features of spots. HiSTaR tends to perform well in identifying spatial domains across multiple datasets from ...
Scientific Research Publishing

Jin, W., Barzilay, R. and Jaakkola, T. (2018) Junction Tree Variational Autoencoder for Molecular Graph Generation. International Conference on Machine Learning, Stockholm, 10 ...

ABSTRACT: Predicting molecular properties is essential for advancing for advancing drug discovery and design. Recently, Graph Neural Networks (GNNs) have gained prominence due to their ability to ...