GitHub

Variational inference for approximate objective priors using neural networks

In Bayesian statistics, the choice of the prior can have an important influence on the posterior and the parameter estimation, especially when few data samples are available. To limit the added ...
GitHub

Variational autoencoder with Kolmogorov-Arnold Network for spatial domain detection.

Experiments were executed on NVIDIA A40 of 46068MiB memory in linux with torch==2.1.0+cu121, torch_geometric==2.3.1, torch-sparse==0.6.18+pt21cu121, and torchvision==0.16.0+cu121. The stkan is an ...
IEEE

Application of variational autoencoder network to real-time prediction of steel crown in the hot strip rolling mill process

Abstract: In the hot strip rolling mill (HSRM) process, accurate prediction and control of the strip crown are critical for quality assurance. In order to cope with this challenge, this study designed ...
Frontiers

Quantifying the spatial patterns of retinal ganglion cell loss and progression in optic neuropathy by applying a deep learning variational autoencoder approach to optical ...

Introduction: Glaucoma, optic neuritis (ON), and non-arteritic anterior ischemic optic neuropathy (NAION) produce distinct patterns of retinal ganglion cell (RGC) damage. We propose a booster ...
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 ...
IEEE

Optimizing Variational Graph Autoencoder for Community Detection

Abstract: Variational Graph Autoencoders (VGAE) has recently been a popular framework of choice for learning representations on graphs. Its inception has allowed models to achieve state-of-the-art ...