40 citations · 122 across the 19 of their papers we have counts for
6 papers · 1 filter
Graph Deep Learning for Time Series Forecasting
Andrea Cini, Ivan Marisca, Daniele Zambon +1
Graph deep learning methods have become popular tools to process collections of correlated time series. Unlike traditional multivariate forecasting methods, graph-based predictors…
Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting
Andrea Cini, Danilo Mandic, Cesare Alippi
Relationships among time series can be exploited as inductive biases in learning effective forecasting models. In hierarchical time series, relationships among subsets of sequences…
Feudal Graph Reinforcement Learning
Tommaso Marzi, Arshjot Khehra, Andrea Cini +1
Graph-based representations and message-passing modular policies constitute prominent approaches to tackling composable control problems in reinforcement learning (RL). However, as…
Object-Centric Relational Representations for Image Generation
Luca Butera, Andrea Cini, Alberto Ferrante +1
Conditioning image generation on specific features of the desired output is a key ingredient of modern generative models. However, existing approaches lack a general and unified wa…
Taming Local Effects in Graph-based Spatiotemporal Forecasting
Andrea Cini, Ivan Marisca, Daniele Zambon +1
Spatiotemporal graph neural networks have shown to be effective in time series forecasting applications, achieving better performance than standard univariate predictors in several…
Graph State-Space Models and Latent Relational Inference
Daniele Zambon, Andrea Cini, Cesare Alippi
State-space models effectively model multivariate time series by updating over time a representation of the system state from which predictions are made. The state representation i…