Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 4 of 4 for “"Convolution networks"”.
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Spatial-Temporal Data Modeling with Graph Neural Networks
… four major shortcomings: 1) Most graph neural networks only focus on the low frequency band of graph signals; 2) Current studies assume the graph structure of data reflects the genuine dependency relationships among nodes; 3) Existing studies on spatial-temporal graph neural networks are not …
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Visual question answering using external knowledge
… we develop an entity graph and use a graph convolutional network to `reason' about the correct answer by jointly considering all entities. We show on the FVQA dataset that this leads to an improvement in accuracy of around 7% compared to the state-of-the-art.
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User behavior modeling: Towards solving the duality of interpretability and precision
… the quality of the answer. We also develop convolution operators to encode these semantically different graphs and later merge them using boosting. We also proposed an alternative approach to incorporate user behavioral information by jointly estimating the latent behavioral representations …
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Machine Learning Methods for Brain Image Analysis
… compound interactions between complex synaptic networks inside the brain remain some of the most challenging problems in neuroscience. Lack or abundance of data, shortage of manpower along with heterogeneity of data following from various species all served as an added complexity to the already …