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 20 of 24 for “"Graph Convolutional Networks"”.
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Dynamic Spatio-Temporal Graph Convolutional Networks
… have seen impressive gains in the performance of graph learning as a paradigm for spatial learning problems. Some recent work has explored the intersection of these two fields but often assumes that the underlying graph structure is static. We introduce Dynamic Spatio-Temporal Graph Convolution …
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Leveraging Basis Alignment to create a Generalized Multi-Relational Graph Convolution Network in the Federated Setting
Knowledge graphs have seen a significant rise in popularity and usage in recent years with many real-world applications taking advantage of their ability to model interlinked data easily. In general, many institutions maintain their own knowledge graphs, however these graphs tend to suffer from …
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Commonsense for Zero-Shot Natural Language Video Localization
… enhancement module. Our approach employs Graph Convolutional Networks (GCN) to encode commonsense information extracted from a knowledge graph, conditioned on the video, and cross-attention mechanisms to enhance the encoded video and pseudo-query vectors prior to localization. Through …
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Graph structures, random walks, and all that : learning graphs with jumping knowledge networks
Graph representation learning aims to extract high-level features from the graph structures and node features, in order to make predictions about the nodes and the graphs. Applications include predicting chemical properties of drugs, community detection in social networks, and modeling interactions …
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Protein Function Prediction Using Graph Convolutional Network
… integrating protein language models (PLMs) and graph convolutional networks (GCNs), addressing the limitations of traditional methods that rely heavily on sequence similarity. The proposed model leverages diverse protein features, including sequences, protein-protein interaction (PPI) networks, …
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A Multimodal Graph Convolutional Approach to Predict Genes Associated with Rare Genetic Diseases
… and genes, and develop an approach based on graph convolutional networks. We show how our model design considerations impact prediction performance. We demonstrate that our approach outperforms simpler graph machine learning and traditional machine learning approaches, as well as a …
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Graph Learning and Optimization for Irregular-Structured Signal Processing
Graph Signal Processing (GSP) extends harmonic analysis tools, such as Fourier transforms and wavelets, to discrete signals defined on finite graphs, enabling tasks like signal denoising, prediction, and interpolation on irregular domains. A critical first step in GSP is to learn an appropriate …
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MEng Thesis: Incorporating Structured Commonsense into Language Models
… in text. We harness the power of relational graph convolutional networks (RGCNs) to encode meaningful commonsense information from graphs and introduce 3 simple methods to inject this knowledge to improve contextual language representations from transformer-based language models. We show that …
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Improving Text Classification Using Graph-based Methods
… and high ambiguity that result from Arabic orthography. Thus, Arabic natural language processing is challenging. Several studies employ Long Short- Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), but Graph Convolutional Networks (GCNs) have not yet been investigated for the …
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Encoding parameter and structural efficiency in deep learning
… and more recently relational learning from graph-structured data. The main reason for this success is an increase in the availability of computational power, which allows for deep and highly parameterized neural network architectures which can learn complex feature transformations from raw …
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Graph Neural Networks: Techniques and Applications
… to the geometry of the data represented by a graph. Typical applications include social networks, transportation networks, the spread of epidemic disease, brain's neuronal networks, gene data on biological regulatory networks, telecommunication networks, knowledge graph, which are lying on the …
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Data-Driven Routing for Autonomous Trucks: Learning from Human Behavior with Context Awareness and Privacy Protection
… develops the Multi-Task Context-Based GRU Graph Convolutional Network (MT-C2G) for predicting truck traffic under extreme weather. MT-C2G integrates Graph Convolutional Networks (GCNs) for spatial structure, Gated Recurrent Units (GRUs) for temporal dynamics, and attention mechanisms for …
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Mine the node association: Dig into the essence of graphs
In the era of big data, graph arises as a crucial data structure. Compared with other types of data, the essence of graphs large lies in node association, which represents unique, informative and important relation between nodes. Recently, mining the node association has attracted remarkable …
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GNN-Enhanced Hierarchical Federated Learning in Device-to-Device Networks
… of intelligent devices and advanced wireless networks has resulted in an explosive growth of data generated at the network edge, creating new opportunities for data-driven services while posing fundamental challenges in privacy preservation, communication efficiency, and adaptability. …
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Deep Learning for Brain Structural Connectivity Analysis: From Tissue Segmentation to Tractogram Alignment
… investigate the structures of WM through tractography techniques, obtaining a virtual representation of the WM pathways called tractogram. Since the tractogram is a collection of digital fibers representing the neuronal axons connecting the brain's cortical areas, it is the fundamental element …
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Accelerating graph computation with system optimizations and algorithmic design
… data in today's world can be represented in a graph form, and these graphs can then be used as input to graph applications to derive useful information, such as shortest paths in a road network, similarity between drugs in a drug-protein network, persons of interest in a social network, or …
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Structure-aware Deep Learning
Graph structures permeate the digital landscape in explicit and implicit forms. They connect or construct artifacts by combining semantic and structural information. We also observe them in the systems designed to process this data, in their learning algorithms and the very nature of the tasks they …
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End-to-end Contextual Speech Recognition and Understanding
… purpose. TCPGen can be further equipped with graph neural networks (GNN) by exploiting the tree structure of the biasing list. GNN encodings provide more powerful node representations in the prefix tree of TCPGen, allowing for "lookahead" functionality where each node contains not only its own …
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Supervised Inference of Gene Regulatory Networks
… slow and painstaking and does not scale to large networks. In this thesis, we study the problem of inferring GRNs automatically from gene expression data. Recent data-driven approaches to infer GRNs increasingly rely on single-cell level RNA-sequencing (scRNA-seq) data. Most of these methods rely …
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Development, evaluation, and In-vitro assessment of artificial intelligence antidiabetic predictive models from α-glucosidase inhibitors
… deep learning models were created using Graph Neural Networks (GNNs) architectures, including Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), Graph Isomorphism Networks (GIN), and Attentive Fingerprints (AFP). The GNNs work directly with molecular graphs, where atoms …
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