A new, densely annotated 3D-text dataset called 3D-GRAND can help train embodied AI, like household robots, to connect ...

Jin, W., Barzilay, R. and Jaakkola, T. (2018) Junction Tree Variational Autoencoder for Molecular Graph Generation. International Conference on Machine Learning, Stockholm, 10-15 July 2018, 2323-2332.

In this work, the Attribute-Embedded Graph Autoencoder (AEGAE) method for community detection is proposed. The AEGAE method for community detection comprises two key functions. Initially, the network ...

This paper proposes a Multi-scale Dilated Variational Graph Convolutional Autoencoder (MG-VAE) model for gas turbine fault diagnosis. The model integrates a multi-scale dilated convolutional attention ...

If too many hidden nodes are used, the autoencoder will essentially memorize the source data -- overfitting the data, and the model won't generalize when previously unseen data is encountered. As a ...

This paper proposes a novel, data-agnostic, model poisoning attack on Federated Learning (FL), by designing a new adversarial graph autoencoder (GAE)-based framework. The attack requires no knowledge ...

GCA-ROM is a library which implements graph convolutional autoencoder architecture as a nonlinear model order reduction strategy. - fpichi/gca-rom. Skip to ... {A Graph Convolutional Autoencoder ...