Essays about: "Dynamic graph representation learning"
Showing result 1 - 5 of 6 essays containing the words Dynamic graph representation learning.
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1. Cyber Threat Detection using Machine Learning on Graphs : Continuous-Time Temporal Graph Learning on Provenance Graphs
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Cyber attacks are ubiquitous and increasingly prevalent in industry, society, and governmental departments. They affect the economy, politics, and individuals. READ MORE
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2. Breast Cancer Histological Grading Using Graph Convolutional Networks
University essay from KTH/Matematik (Avd.)Abstract : Technological advancements have opened up the possibility of digitizing the pathological landscape, enabling deep learning-based methods to analyze digitized tissue samples, i.e., whole slide images (WSIs). Attention has recently shifted toward modeling WSIs as graphs since graph representations can capture dynamic relationships. READ MORE
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3. Traffic Prediction From Temporal Graphs Using Representation Learning
University essay from KTH/Matematisk statistikAbstract : With the arrival of 5G networks, telecommunication systems are becoming more intelligent, integrated, and broadly used. This thesis focuses on predicting the upcoming traffic to efficiently promote resource allocation, guarantee stability and reliability of the network. READ MORE
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4. Dynamic Graph Representation Learning on Enterprise Live Video Streaming Events
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Enterprises use live video streaming as a mean of communication. Streaming high-quality video to thousands of devices in a corporate network is not an easy task; the bandwidth requirements often exceed the network capacity. READ MORE
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5. Real-time Anomaly Detection on Financial Data
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : This work presents an investigation of tailoring Network Representation Learning (NRL) for an application in the Financial Industry. NRL approaches are data-driven models that learn how to encode graph structures into low-dimensional vector spaces, which can be further exploited by downstream Machine Learning applications. READ MORE