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Found 16 papers, 9 papers with code
Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements
Catheter ablation of Atrial Fibrillation (AF) consists of a one-size-fits-all treatment with limited success in persistent AF.
Relational Conformal Prediction for Correlated Time Series
We fill this void by introducing a novel conformal prediction method based on graph deep learning operators.
On the Regularization of Learnable Embeddings for Time Series Processing
In this paper, we address this issue by investigating methods to regularize the learning of local learnable embeddings for time series processing.
Graph-based Virtual Sensing from Sparse and Partial Multivariate Observations
Virtual sensing techniques allow for inferring signals at new unmonitored locations by exploiting spatio-temporal measurements coming from physical sensors at different locations.
Graph Deep Learning for Time Series Forecasting
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.
Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting
Relationships among time series can be exploited as inductive biases in learning effective forecasting models.
Feudal Graph Reinforcement Learning
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.
Object-Centric Relational Representations for Image Generation
In particular, we propose a methodology to condition the generation of objects in an image on the attributed graph representing their structure and the associated semantic information.
Taming Local Effects in Graph-based Spatiotemporal Forecasting
Spatiotemporal graph neural networks have shown to be effective in time series forecasting applications, achieving better performance than standard univariate predictors in several settings.
Graph state-space models
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.
Scalable Spatiotemporal Graph Neural Networks
The training procedure can then be parallelized node-wise by sampling the node embeddings without breaking any dependency, thus enabling scalability to large networks.
Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations
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.
Sparse Graph Learning from Spatiotemporal Time Series
Outstanding achievements of graph neural networks for spatiotemporal time series analysis show that relational constraints introduce an effective inductive bias into neural forecasting architectures.
A Graph Deep Learning Framework for High-Level Synthesis Design Space Exploration
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.
Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks
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.
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Deep Reinforcement Learning with Weighted Q-Learning
In this regard, Weighted Q-Learning (WQL) effectively reduces bias and shows remarkable results in stochastic environments.
