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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...
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04/11/2023
Feudal Graph Reinforcement Learning
We focus on learning composable policies to control a variety of physica...
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03/26/2023
Relational Inductive Biases for Object-Centric Image Generation
Conditioning image generation on specific features of the desired output...
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02/08/2023
Taming Local Effects in Graph-based Spatiotemporal Forecasting
Spatiotemporal graph neural networks have shown to be effective in time ...
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01/04/2023
Graph state-space models
State-space models constitute an effective modeling tool to describe mul...
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09/14/2022
Scalable Spatiotemporal Graph Neural Networks
Neural forecasting of spatiotemporal time series drives both research an...
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05/26/2022
Sparse Graph Learning for Spatiotemporal Time Series
Outstanding achievements of graph neural networks for spatiotemporal tim...
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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...
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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...
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07/31/2021
Multivariate Time Series Imputation by Graph Neural Networks
Dealing with missing values and incomplete time series is a labor-intens...
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03/20/2020