Essays about: "graph neural networks"

Showing result 11 - 15 of 82 essays containing the words graph neural networks.

  1. 11. Link Prediction Using Learnable Topology Augmentation

    University essay from KTH/Matematik (Avd.)

    Author : Tori Leatherman; [2023]
    Keywords : Network Analysis; Inductive Link Prediction; Learnable Augmentation; Graph Neural Networks; Multilayer Perceptrons; Nätverksanalys; Induktiv Länkförutsägelse; Inlärningsbar Förstärkning; Grafiska Neurala Nätverk; Flerskiktsperceptroner;

    Abstract : Link prediction is a crucial task in many downstream applications of graph machine learning. Graph Neural Networks (GNNs) are a prominent approach for transductive link prediction, where the aim is to predict missing links or connections only within the existing nodes of a given graph. READ MORE

  3. 12. RNN-based Graph Neural Network for Credit Load Application leveraging Rejected Customer Cases

    University essay from Högskolan i Halmstad/Akademin för informationsteknologi

    Author : Oskar Nilsson; Benjamin Lilje; [2023]
    Keywords : Machine Learning; Deep Learning; Reject Inference; GNN; GCN; Graph Neural Networks; RNN; Recursive Neural Network; LSTM; Semi-Supervised Learning; Encoding; Decoding; Feature Elimination;

    Abstract : Machine learning plays a vital role in preventing financial losses within the banking industry, and still, a lot of state of the art and industry-standard approaches within the field neglect rejected customer information and the potential information that they hold to detect similar risk behavior.This thesis explores the possibility of including this information during training and utilizing transactional history through an LSTM to improve the detection of defaults. READ MORE

  4. 13. Machine Learning-Based Instruction Scheduling for a DSP Architecture Compiler : Instruction Scheduling using Deep Reinforcement Learning and Graph Convolutional Networks

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Lucas Alava Peña; [2023]
    Keywords : Instruction Scheduling; Deep reinforcement Learning; Compilers; Graph Convolutional Networks; Schemaläggning av instruktioner; Deep Reinforcement Learning; kompilatorer; grafkonvolutionella nätverk;

    Abstract : Instruction Scheduling is a back-end compiler optimisation technique that can provide significant performance gains. It refers to ordering instructions in a particular order to reduce latency for processors with instruction-level parallelism. READ MORE

  5. 14. Reducing Power Consumption For Signal Computation in Radio Access Networks : Optimization With Linear Programming and Graph Attention Networks

    University essay from Linköpings universitet/Programvara och system

    Author : Martin Nordberg; [2023]
    Keywords : Cloud RAN; Constrained optimization; Mixed integer linear programming; MILP; Machine learning; Graph neural network; Graph attention network; GAT;

    Abstract : There is an ever-increasing usage of mobile data with global traffic having reached 115 exabytes per month at the end of 2022 for mobile data traffic including fixed wireless access. This is projected to grow up to 453 exabytes at the end of 2028, according to Ericssons 2022 mobile data traffic outlook report. READ MORE

  6. 15. Time synchronization error detection in a radio access network

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Moulika Madana; [2023]
    Keywords : GNSS - Global Navigation Satellite System; OAS - Over-the air-synchronization; PRTC - primary reference time clock; PTP - precision time protocol; Gauss Jordan elimination; GNN- Graph Neural Network; GNSS -Globalt navigationssatellitsystem; OAS - Över-the-air tidssynkronisering; PRTC - Primär referenstidklocka; PTP - Precisionstidprotokoll; Gauss Jordan eliminering; GNN- Graf neurala nätverk;

    Abstract : Time synchronization is a process of ensuring all the time difference between the clocks of network components(like base stations, boundary clocks, grandmasters, etc.) in the mobile network is zero or negligible. It is one of the important factors responsible for ensuring effective communication between two user-equipments in a mobile network. READ MORE