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Probabilistic logic programming for uncertain reasoning – Complete Phd and Masters Thesis

[ad_1] Introduction Probabilistic logic programming is a powerful framework that combines the expressiveness of logic programming with the ability to reason under uncertainty. This approach has been widely used in various fields such as artificial…

Inductive logic programming for rule learning – Complete Phd and Masters Thesis

[ad_1] Introduction Inductive logic programming (ILP) is a subfield of machine learning and artificial intelligence that combines logic programming and traditional machine learning techniques to induce logic programs from data. Rule learning, which involves the…

Commonsense reasoning for general understanding – Complete Phd and Masters Thesis

[ad_1] Introduction Commonsense reasoning is a fundamental aspect of human cognition that allows individuals to make inferences, draw conclusions, and navigate the complexities of everyday life based on a set of shared beliefs and assumptions.…

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 generative models for graph synthesis – Complete Phd and Masters Thesis

[ad_1] Introduction Graph generative models have gained significant attention in recent years due to their ability to synthesize graph structures with desired properties. These models have various applications in fields such as social network analysis,…

Graph attention networks for node importance – Complete Phd and Masters Thesis

[ad_1] Introduction In recent years, there has been an increasing interest in graph neural networks, which are powerful tools for learning representations of graph-structured data. Graph attention networks, a type of graph neural network, have…

Neuroevolution for learning and optimization – Complete Phd and Masters Thesis

[ad_1] Introduction Neuroevolution is a computational method that combines the principles of artificial neural networks and evolutionary algorithms to enable learning and optimization in complex systems. By leveraging the adaptability of neural networks and the…

Evolutionary neural networks for adaptation – Complete Phd and Masters Thesis

[ad_1] Introduction In recent years, the field of artificial intelligence has seen significant advancements, particularly in the area of neural networks. Neural networks are computational models inspired by the human brain that have the ability…

Neural architecture search for optimal design – Complete Phd and Masters Thesis

[ad_1] Introduction Neural architecture search (NAS) has emerged as a powerful technique for automatically designing neural network architectures to achieve optimal performance on a given task. The main goal of NAS is to replace the…

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