439 citations · 1.4k across the 66 of their papers we have counts for
9 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…
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
Ming Jin, Huan Yee Koh, Qingsong Wen +5
Time series are the primary data type used to record dynamic system measurements and generated in great volume by both physical sensors and online processes (virtual sensors). Time…
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…
Graph Kalman Filters
Cesare Alippi, Daniele Zambon
The well-known Kalman filters model dynamical systems by relying on state-space representations with the next state updated, and its uncertainty controlled, by fresh information as…