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Analogical reasoning for knowledge transfer – Complete Phd and Masters Thesis

[ad_1] Introduction Analogical reasoning has been recognized as a powerful cognitive process that enables individuals to transfer knowledge from one domain to another. This mechanism allows us to draw parallels between similar situations and apply…

Knowledge base completion for missing facts – Complete Phd and Masters Thesis

[ad_1] Introduction Knowledge base completion is a fundamental task in the field of knowledge representation and reasoning. It aims to automatically infer missing facts in a knowledge base by leveraging existing knowledge and relationships. The…

Ontology learning for domain modeling – Complete Phd and Masters Thesis

[ad_1] Introduction Ontology learning is a crucial aspect of knowledge engineering that involves the automatic extraction of domain-specific knowledge from unstructured text or data. It plays a vital role in the development of domain models,…

Knowledge graphs for structured knowledge representation – Complete Phd and Masters Thesis

[ad_1] Introduction: Knowledge graphs have emerged as a powerful tool for representing structured knowledge in a variety of domains. These graphs provide a flexible and scalable way to capture relationships between entities and concepts, making…

Graph convolutional networks for node classification – Complete Phd and Masters Thesis

[ad_1] Introduction Graph Convolutional Networks (GCNs) have gained significant attention in recent years due to their effectiveness in node classification tasks on graph-structured data. GCNs are a type of neural network designed to operate on…

Recurrent relational networks for relational reasoning – Complete Phd and Masters Thesis

[ad_1] Introduction: Recurrent relational networks have emerged as a powerful tool for relational reasoning in various domains such as natural language processing, computer vision, and robotics. These networks have the ability to capture complex relationships…

Incremental learning for growing networks – Complete Phd and Masters Thesis

[ad_1] Introduction: In the era of big data, the continuous growth of networks such as social networks, communication networks, and the Internet of Things (IoT) has posed significant challenges for traditional machine learning algorithms. Traditional…

Federated neural networks for distributed learning – Complete Phd and Masters Thesis

[ad_1] Introduction Federated learning is a decentralized machine learning approach that allows multiple parties to collaboratively train a global model without sharing their data. This approach addresses privacy concerns and data security issues associated with…

Quantum neural networks for exponential speedup – Complete Phd and Masters Thesis

[ad_1] Introduction Quantum computing has emerged as a revolutionary technology with the potential to solve complex problems that are intractable for classical computers. In recent years, there has been significant interest in leveraging the power…

Spiking neural networks for energy efficiency – Complete Phd and Masters Thesis

[ad_1] Introduction: In the field of artificial neural networks, spiking neural networks (SNNs) have gained significant attention for their potential in achieving high energy efficiency. SNNs are biologically inspired neural networks that operate based on…

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