Daniele Zambon

Filippo Maria Bianchi

IEEE Trans. Neural Networks Learn. Syst., February, 2024

Learning Latent Graph Structures and their Uncertainty.

[BibT_eX]

[DOI]

Alessandro Manenti

CoRR, 2024

Temporal Graph ODEs for Irregularly-Sampled Time Series.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

Graph-based Virtual Sensing from Sparse and Partial Multivariate Observations.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Sparse Graph Learning from Spatiotemporal Time Series.

[BibT_eX]

[DOI]

Andrea Cini

J. Mach. Learn. Res., 2023

Graph Deep Learning for Time Series Forecasting.

[BibT_eX]

[DOI]

CoRR, 2023

Graph Kalman Filters.

[BibT_eX]

[DOI]

CoRR, 2023

Where and How to Improve Graph-based Spatio-temporal Predictors.

[BibT_eX]

[DOI]

CoRR, 2023

Graph state-space models.

[BibT_eX]

[DOI]

CoRR, 2023

Taming Local Effects in Graph-based Spatiotemporal Forecasting.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Graph Representation Learning.

[BibT_eX]

[DOI]

Proceedings of the 31st European Symposium on Artificial Neural Networks, 2023

2022

Sparse Graph Learning for Spatiotemporal Time Series.

[BibT_eX]

[DOI]

Andrea Cini

CoRR, 2022

AZ-whiteness test: a test for uncorrelated noise on spatio-temporal graphs.

[BibT_eX]

[DOI]

CoRR, 2022

AZ-whiteness test: a test for signal uncorrelation on spatio-temporal graphs.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Graph iForest: Isolation of anomalous and outlier graphs.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2022

Understanding Catastrophic Forgetting of Gated Linear Networks in Continual Learning.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2022

Deep Learning for Graphs.

[BibT_eX]

[DOI]

Proceedings of the 30th European Symposium on Artificial Neural Networks, 2022

2021

Erkennung von Anomalien und Veränderung in Graphsequenzen.

[BibT_eX]

[DOI]

Proceedings of the Ausgezeichnete Informatikdissertationen 2021., 2021

Graph Edit Networks.

[BibT_eX]

[DOI]

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature Manifolds.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2020

Graph Random Neural Features for Distance-Preserving Graph Representations.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Change-Point Methods on a Sequence of Graphs.

[BibT_eX]

[DOI]

IEEE Trans. Signal Process., 2019

Distance-Preserving Graph Embeddings from Random Neural Features.

[BibT_eX]

[DOI]

CoRR, 2019

Autoregressive Models for Sequences of Graphs.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2019

2018

Concept Drift and Anomaly Detection in Graph Streams.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2018

Learning Graph Embeddings on Constant-Curvature Manifolds for Change Detection in Graph Streams.

[BibT_eX]

[DOI]

CoRR, 2018

Anomaly and Change Detection in Graph Streams through Constant-Curvature Manifold Embeddings.

[BibT_eX]

[DOI]

Proceedings of the 2018 International Joint Conference on Neural Networks, 2018

2017

Detecting changes in sequences of attributed graphs.

[BibT_eX]

[DOI]