Sökning: "homogena nätverk"

Visar resultat 1 - 5 av 18 uppsatser innehållade orden homogena nätverk.

1. 1. Domain Knowledge and Representation Learning for Centroid Initialization in Text Clustering with k-Means : An exploratory study

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :David Yu; [2023]
   Nyckelord :Natural language processing; Sentiment analysis; Clustering; Language model; Transformer; Heuristic; Språkteknologi; Sentimentanalys; Klustering; Språkmodell; Transformer; Heuristik;

   Sammanfattning : Text clustering is a problem where texts are partitioned into homogeneous clusters, such as partitioning them based on their sentiment value. Two techniques to address the problem are representation learning, in particular language representation models, and clustering algorithms. LÄS MER

3. 2. 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)

   Författare :Jakub Reha; [2023]
   Nyckelord :Graph neural networks; Temporal graphs; Benchmark datasets; Anomaly detection; Heterogeneous graphs; Provenance graphs; Grafiska neurala nätverk; temporala grafer; benchmark-datauppsättningar; anomalidetektering; heterogena grafer; härkomstgrafer;

   Sammanfattning : Cyber attacks are ubiquitous and increasingly prevalent in industry, society, and governmental departments. They affect the economy, politics, and individuals. LÄS MER

4. 3. Link Prediction Using Learnable Topology Augmentation

   Master-uppsats, KTH/Matematik (Avd.)

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

   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. 4. Artificial Neural Networks and Inductive Biases for Multi-Instance Multi-Modal Tabular Data : A Case Study for Default Probability Estimation in Small-to-Medium Enterprise Lending

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Gustav Röhss; [2022]
   Nyckelord :;

   Sammanfattning : The success of artificial neural networks in homogeneous data domains such as images, textual data, and audio and other signals has had considerable impact on Machine Learning and science in general. The domain of heterogeneous tabular data, while arguably much more common, remains much less explored with regards to artificial neural networks and deep learning. LÄS MER

7. 5. A Deep-Learning-Based Approach for Stiffness Estimation of Deformable Objects

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Nan Yang; [2022]
   Nyckelord :Robotic grasping; Deformable objects; Deformation modeling; Stiffness estimation; Deep learning; Robotgrepp; Deformerbara föremål; Deformationsmodellering; Styvhetsuppskattning; Djup lärning;

   Sammanfattning : Object deformation is an essential factor for the robot to manipulate the object, as the deformation impacts the grasping of the deformable object either positively or negatively. One of the most challenging problems with deformable objects is estimating the stiffness parameters such as Young’s modulus and Poisson’s ratio. LÄS MER