Andrea Cini | DeepAI

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∙ 05/30/2023

Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting

Existing relationships among time series can be exploited as inductive b...

0 Andrea Cini, et al. ∙

∙ 04/11/2023

Feudal Graph Reinforcement Learning

We focus on learning composable policies to control a variety of physica...

1 Tommaso Marzi, et al. ∙

∙ 03/26/2023

Relational Inductive Biases for Object-Centric Image Generation

Conditioning image generation on specific features of the desired output...

29 Luca Butera, et al. ∙

∙ 02/08/2023

Taming Local Effects in Graph-based Spatiotemporal Forecasting

Spatiotemporal graph neural networks have shown to be effective in time ...

8 Andrea Cini, et al. ∙

∙ 01/04/2023

Graph state-space models

State-space models constitute an effective modeling tool to describe mul...

1 Daniele Zambon, et al. ∙

∙ 09/14/2022

Scalable Spatiotemporal Graph Neural Networks

Neural forecasting of spatiotemporal time series drives both research an...

2 Andrea Cini, et al. ∙

∙ 05/26/2022

Sparse Graph Learning for Spatiotemporal Time Series

Outstanding achievements of graph neural networks for spatiotemporal tim...

0 Andrea Cini, et al. ∙

∙ 05/26/2022

Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations

Modeling multivariate time series as temporal signals over a (possibly d...

0 Ivan Marisca, et al. ∙

∙ 11/29/2021

A Graph Deep Learning Framework for High-Level Synthesis Design Space Exploration

The design of efficient hardware accelerators for high-throughput data-p...

0 Lorenzo Ferretti, et al. ∙

∙ 07/31/2021

Multivariate Time Series Imputation by Graph Neural Networks

Dealing with missing values and incomplete time series is a labor-intens...

0 Andrea Cini, et al. ∙

∙ 03/20/2020

Deep Reinforcement Learning with Weighted Q-Learning

Overestimation of the maximum action-value is a well-known problem that ...

0 Andrea Cini, et al. ∙

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