Zobrazeno 1 - 10
of 180
pro vyhledávání: '"Gama, Fernando"'
Accurate estimation of the states of a nonlinear dynamical system is crucial for their design, synthesis, and analysis. Particle filters are estimators constructed by simulating trajectories from a sampling distribution and averaging them based on th
Externí odkaz:
http://arxiv.org/abs/2302.01174
Filters are fundamental in extracting information from data. For time series and image data that reside on Euclidean domains, filters are the crux of many signal processing and machine learning techniques, including convolutional neural networks. Inc
Externí odkaz:
http://arxiv.org/abs/2211.08854
Designing distributed optimal controllers subject to communication constraints is a difficult problem unless structural assumptions are imposed on the underlying dynamics and information exchange structure, e.g., sparsity, delay, or spatial invarianc
Externí odkaz:
http://arxiv.org/abs/2210.15847
Optimal power flow (OPF) is a critical optimization problem that allocates power to the generators in order to satisfy the demand at a minimum cost. Solving this problem exactly is computationally infeasible in the general case. In this work, we prop
Externí odkaz:
http://arxiv.org/abs/2210.09277
In this paper we study the stability properties of aggregation graph neural networks (Agg-GNNs) considering perturbations of the underlying graph. An Agg-GNN is a hybrid architecture where information is defined on the nodes of a graph, but it is pro
Externí odkaz:
http://arxiv.org/abs/2207.03678
Graph filtering is the cornerstone operation in graph signal processing (GSP). Thus, understanding it is key in developing potent GSP methods. Graph filters are local and distributed linear operations, whose output depends only on the local neighborh
Externí odkaz:
http://arxiv.org/abs/2202.10649
Power allocation is one of the fundamental problems in wireless networks and a wide variety of algorithms address this problem from different perspectives. A common element among these algorithms is that they rely on an estimation of the channel stat
Externí odkaz:
http://arxiv.org/abs/2110.07471
Particle filtering is used to compute good nonlinear estimates of complex systems. It samples trajectories from a chosen distribution and computes the estimate as a weighted average. Easy-to-sample distributions often lead to degenerate samples where
Externí odkaz:
http://arxiv.org/abs/2110.02915
Graph convolutional neural networks (GCNNs) are popular deep learning architectures that, upon replacing regular convolutions with graph filters (GFs), generalize CNNs to irregular domains. However, classical GFs are prone to numerical errors since t
Externí odkaz:
http://arxiv.org/abs/2110.00844
Graph neural networks (GNNs) are naturally distributed architectures for learning representations from network data. This renders them suitable candidates for decentralized tasks. In these scenarios, the underlying graph often changes with time due t
Externí odkaz:
http://arxiv.org/abs/2107.09203