We propose a new model for metaphor detection in which an expectation component estimates representations of expected word meanings in a given context, whereas a realization component computes representations of targe...
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Dynamic Bayesian Networks (DBNs) are useful tools for modelling complex systems whose network representations can be elicited a priori or learnt from data. In this paper, a maximum likelihood Doubly-Iterative Expectat...
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This work addresses submodular maximization problems, a widely-used mathematical tool to model many real-world decisions. Though this set of problems is NP-Hard, a well-known result is that a distributed greedy algori...
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This paper addresses the challenge of detecting multiple targets embedded in Gaussian noise with unknown range positions and angles of arrival. To this end, we introduce the signal classification model of interest res...
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Staged trees are probabilistic graphical models capable of representing any class of non-symmetric independence via a coloring of their vertices. Several structural learning routines have been defined and implemented ...
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In lunar exploration and research, the accurate identification and localization of lunar surface features is of great scientific significance and application value. However, target detection in lunar surface images fa...
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Accurate time series forecasting is critical in various fields, including resource allocation and crime prevention. While traditional approaches often focus on continuous data, count data forecasting, especially with ...
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Unmanned aerial vehicle (UAV) video transmission has been extensively applied in many crucial fields. However, the problem of designing a rate-distortion (R-D) model for UAV video transmission, which is essential for ...
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Traditionally, graph neural networks have been trained using a single observed graph. However, the observed graph represents only one possible realization. In many applications, the graph may encounter uncertainties, ...
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Traditionally, graph neural networks have been trained using a single observed graph. However, the observed graph represents only one possible realization. In many applications, the graph may encounter uncertainties, such as having erroneous or missing edges, as well as edge weights that provide little informative value. To address these challenges and capture additional information previously absent in the observed graph, we introduce latent variables to parameterize and generate multiple graphs. The parameters follow an unknown distribution to be estimated. We propose a formulation in terms of maximum likelihood estimation of the network parameters. Therefore, it is possible to devise an algorithm based on expectation-maximization (EM). Specifically, we iteratively determine the distribution of the graphs using a Markov Chain Monte Carlo (MCMC) method, incorporating the principles of PAC-Bayesian theory. Numerical experiments demonstrate improvements in performance against baseline models on node classification for both heterogeneous and homogeneous graphs. Copyright 2024 by the author(s)
This study addresses the issue of blastomere cleavage timing, which is traditionally deemed the most important indicator in embryo quality assessment. We propose a novel quantization method for analyzing Time-lapse In...
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