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Spatial-temporal graph neural network with autoencoder pretraining for intrusion detection in healthcare IoT ecosystems. [PDF]

Sci Rep
Tanvir MIM, Nadia NY, Rabby HR, Arif MH, Nur K, Ghose D.   +5 more
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Map-free vehicle trajectory prediction method based on heterogeneous graphs and dynamic scene constraints. [PDF]

Sci Rep
Liu H, Bao Y, Hou Y, Wang H, Shi Q.
europepmc   +1 more source

Benchmarking Multimodal Deep Fusion Strategies for Heterogeneous Neuroimaging and Cognitive Data Using a Controlled Sex Classification Task. [PDF]

Brain Sci
Camastra C, Pelagi A, Quattrone A, Sarica A.   +3 more
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iAODE for benchmarking and continuum modeling of single-cell chromatin accessibility. [PDF]

Commun Biol
Fu Z, Chen C, Wang S, Wang J, Chen S.
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MultiPert: An adversarial alignment and dual attention framework for single-cell multi-omics perturbation prediction. [PDF]

PLoS Comput Biol
Zhao M, Tang X, Li J, Liang C, Tang J, Guo F.   +5 more
europepmc   +1 more source

GeneCytNet: an interpretable deep learning framework for rheumatoid arthritis classification and <i>in silico</i> cytokine perturbation modeling. [PDF]

Front Immunol
Chen C, Li D, Xu L.
europepmc   +1 more source

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Scalable Graph Convolutional Variational Autoencoders

2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI), 2021
Autoencoders are widely used for self-supervised representation learning. Variational autoencoders (VAEs), a special type of autoencoders, are proven to be effective in estimating the underlying probability distribution of the training data. Even though VAEs are well explored in many application domains, their utilization for graph-structured data is ...
Unyi, Dániel, Gyires-Tóth, Bálint
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VACA: Designing Variational Graph Autoencoders for Causal Queries

Proceedings of the AAAI Conference on Artificial Intelligence, 2022
In this paper, we introduce VACA, a novel class of variational graph autoencoders for causal inference in the absence of hidden confounders, when only observational data and the causal graph are available. Without making any parametric assumptions, VACA mimics the necessary properties of a Structural Causal Model (SCM) to provide a flexible and ...
Sanchez-Martin, P., Rateike, M., Valera, I.   +2 more
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