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pro vyhledávání: '"Tenorio, Victor M."'
Graph neural networks (GNNs) have become a workhorse approach for learning from data defined over irregular domains, typically by implicitly assuming that the data structure is represented by a homophilic graph. However, recent works have revealed th
Externí odkaz:
http://arxiv.org/abs/2409.08676
This paper introduces a probabilistic approach for tracking the dynamics of unweighted and directed graphs using state-space models (SSMs). Unlike conventional topology inference methods that assume static graphs and generate point-wise estimates, ou
Externí odkaz:
http://arxiv.org/abs/2409.08238
Autor:
Jiang, Liuyuan, Xiao, Quan, Tenorio, Victor M., Real-Rojas, Fernando, Marques, Antonio G., Chen, Tianyi
Interest in bilevel optimization has grown in recent years, partially due to its applications to tackle challenging machine-learning problems. Several exciting recent works have been centered around developing efficient gradient-based algorithms that
Externí odkaz:
http://arxiv.org/abs/2406.10148
Graph Neural Networks (GNNs) have emerged as a notorious alternative to address learning problems dealing with non-Euclidean datasets. However, although most works assume that the graph is perfectly known, the observed topology is prone to errors ste
Externí odkaz:
http://arxiv.org/abs/2312.06557
Blind deconvolution over graphs involves using (observed) output graph signals to obtain both the inputs (sources) as well as the filter that drives (models) the graph diffusion process. This is an ill-posed problem that requires additional assumptio
Externí odkaz:
http://arxiv.org/abs/2309.09063
Node features bolster graph-based learning when exploited jointly with network structure. However, a lack of nodal attributes is prevalent in graph data. We present a framework to recover completely missing node features for a set of graphs, where we
Externí odkaz:
http://arxiv.org/abs/2309.09068
When facing graph signal processing tasks, the workhorse assumption is that the graph describing the support of the signals is known. However, in many relevant applications the available graph suffers from observation errors and perturbations. As a r
Externí odkaz:
http://arxiv.org/abs/2210.08488
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
Publikováno v:
In Transportation Research Procedia 2021 58:463-470
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