1 code implementation • 19 Feb 2024 • Giovanni De Felice, Andrea Cini, Daniele Zambon, Vladimir V. Gusev, Cesare Alippi
Virtual sensing techniques allow for inferring signals at new unmonitored locations by exploiting spatio-temporal measurements coming from physical sensors at different locations.
no code implementations • 24 Oct 2023 • Andrea Cini, Ivan Marisca, Daniele Zambon, Cesare Alippi
The conditioning can take the form of an architectural inductive bias on the neural forecasting architecture, resulting in a family of deep learning models called spatiotemporal graph neural networks.
no code implementations • 30 May 2023 • Andrea Cini, Danilo Mandic, Cesare Alippi
Existing relationships among time series can be exploited as inductive biases in learning effective forecasting models.
no code implementations • 11 Apr 2023 • Tommaso Marzi, Arshjot Khehra, Andrea Cini, Cesare Alippi
In this work, we propose a novel methodology, named Feudal Graph Reinforcement Learning (FGRL), that addresses such challenges by relying on hierarchical RL and a pyramidal message-passing architecture.
no code implementations • 26 Mar 2023 • Luca Butera, Andrea Cini, Alberto Ferrante, Cesare Alippi
Conditioning image generation on specific features of the desired output is a key ingredient of modern generative models.
1 code implementation • NeurIPS 2023 • Andrea Cini, Ivan Marisca, Daniele Zambon, Cesare Alippi
Spatiotemporal graph neural networks have shown to be effective in time series forecasting applications, achieving better performance than standard univariate predictors in several settings.
no code implementations • 4 Jan 2023 • Daniele Zambon, Andrea Cini, Lorenzo Livi, Cesare Alippi
State-space models constitute an effective modeling tool to describe multivariate time series and operate by maintaining an updated representation of the system state from which predictions are made.
1 code implementation • 14 Sep 2022 • Andrea Cini, Ivan Marisca, Filippo Maria Bianchi, Cesare Alippi
The training procedure can then be parallelized node-wise by sampling the node embeddings without breaking any dependency, thus enabling scalability to large networks.
1 code implementation • NeurIPS 2023 • Andrea Cini, Daniele Zambon, Cesare Alippi
Outstanding achievements of graph neural networks for spatiotemporal time series analysis show that relational constraints introduce an effective inductive bias into neural forecasting architectures.
2 code implementations • 26 May 2022 • Ivan Marisca, Andrea Cini, Cesare Alippi
In particular, we propose a novel class of attention-based architectures that, given a set of highly sparse discrete observations, learn a representation for points in time and space by exploiting a spatiotemporal propagation architecture aligned with the imputation task.
no code implementations • 29 Nov 2021 • Lorenzo Ferretti, Andrea Cini, Georgios Zacharopoulos, Cesare Alippi, Laura Pozzi
The design of efficient hardware accelerators for high-throughput data-processing applications, e. g., deep neural networks, is a challenging task in computer architecture design.
2 code implementations • ICLR 2022 • Andrea Cini, Ivan Marisca, Cesare Alippi
In particular, we introduce a novel graph neural network architecture, named GRIN, which aims at reconstructing missing data in the different channels of a multivariate time series by learning spatio-temporal representations through message passing.
Ranked #1 on Traffic Data Imputation on PEMS-BAY Point Missing
no code implementations • 20 Mar 2020 • Andrea Cini, Carlo D'Eramo, Jan Peters, Cesare Alippi
In this regard, Weighted Q-Learning (WQL) effectively reduces bias and shows remarkable results in stochastic environments.