Sökning: "Grafiska neurala nätverk"
Visar resultat 1 - 5 av 11 uppsatser innehållade orden Grafiska neurala nätverk.
1. Cyber Threat Detection using Machine Learning on Graphs : Continuous-Time Temporal Graph Learning on Provenance Graphs
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Cyber attacks are ubiquitous and increasingly prevalent in industry, society, and governmental departments. They affect the economy, politics, and individuals. LÄS MER
2. Graph Neural Networks for Events Detection in Football
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Tracab’s optical tracking system allows to track the 2-dimensional trajectories of players and ball during a football game. Using this data it is possible to train machine learning models to identify events that happen during the match. LÄS MER
3. Inferring Gene regulatory networks using Graph Neural Networks
Master-uppsats, KTH/GenteknologiSammanfattning : Inom beräkningsbiologin är det snabbt på väg att bli allt vanligare att ta fram genetiska regleringsnätverk (GRN). På grund av storleken på de undersökta nätverken använder många forskare maskininlärning för att härleda GRN från genuttrycksdata, vanligtvis från RNA-seq. LÄS MER
4. Link Prediction Using Learnable Topology Augmentation
Master-uppsats, KTH/Matematik (Avd.)Sammanfattning : 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. LÄS MER
5. The Applicability and Scalability of Graph Neural Networks on Combinatorial Optimization
Master-uppsats, KTH/Matematik (Avd.)Sammanfattning : This master's thesis investigates the application of Graph Neural Networks (GNNs) to address scalability challenges in combinatorial optimization, with a primary focus on the minimum Total Dominating set Problem (TDP) and additionally the related Carrier Scheduling Problem (CSP) in networks of Internet of Things. The research identifies the NP-hard nature of these problems as a fundamental challenge and addresses how to improve predictions on input graphs of sizes much larger than seen during training phase. LÄS MER