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.
Results
Showing 1 to 10 of 10 for “"Graph Attention Networks"”.
-
Understanding tumor cell plasticity in spatial transcriptomics with graph attention networks and walk-based pseudotime analysis
… be probed. We introduce PlastiNet, which uses a graphical attention-based network to create a spatial aware embedding. The utility of our approach is validated in model systems, specifically in the brain and colon, where it successfully identifies biologically relevant neighborhoods and maps …
-
Hybrid AI-driven Approach to Context-Aware Inter-Slice Load Balancing for Cloud-Native Functions in 5G Networks
… interactions for intelligent capabilities. Graph neural networks (GNN) and spatio-temporal multi-head graph attention networks (SP-mGAT) are utilized to generate context-aware embeddings, clustering clients by traffic characteristics into priority labels. These labels feed multi-agent deep …
-
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 …
-
Investigating Tree- and Graph-based Neural Networks for Natural Language Processing Applications
… within NLP applications. By leveraging tree- and graph-based neural networks, this study pioneers a holistic approach that augments language understanding and processing capabilities. Through the fusion of structural and semantic-driven insights, this work tries to explore various NLP applications …
-
Accelerating graph attention network inference on CPUs with layer fusion
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01
-
Enabling AI Copilots for Engineering Design With Parametric, Graph, And Component Inputs
… detailed parametric specifications, assembly graphs, visual references, and textual descriptions. Despite growing interest in generative models for design ideation and exploration, state-of-the-art approaches struggle with incomplete inputs, lack of support for modalities other than text and …
-
Decoding brains by paying attention: An attention-based fMRI task state decoding deep network architecture
… explore and compare the effectiveness of linear, graph-based and attention-based methods for hierarchical classification. Furthermore, we propose a new attention-based network architecture which showcases superior performance to all of our baseline architectures without the use of handcrafted …
-
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 …
-
The resurgence of structure in deep neural networks
Machine learning with deep neural networks ("deep learning") allows for learning complex features directly from raw input data, completely eliminating hand-crafted, "hard-coded" feature extraction from the learning pipeline. This has lead to state-of-the-art performance being achieved across …