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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,…

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…

Neural Turing machines for memory augmentation – Complete Phd and Masters Thesis

[ad_1] Introduction Neural Turing Machines (NTMs) have emerged as a promising approach for memory augmentation in neural networks. Inspired by the architecture of the classical Turing machine, NTMs combine neural networks with external memory components,…

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…

Modular neural networks for compositionality – Complete Phd and Masters Thesis

[ad_1] Introduction Modular neural networks have been gaining attention in recent years due to their ability to enhance compositionality in neural networks. Compositionality refers to the capacity of a system to combine simpler components to…

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…

Continual learning for lifelong adaptation – Complete Phd and Masters Thesis

[ad_1] Introduction Continual learning is a key concept in the field of artificial intelligence and machine learning, emphasizing the ability of a system to adapt and learn continuously from new data and experiences in order…

Neuromorphic computing for brain-inspired architectures – Complete Phd and Masters Thesis

[ad_1] Introduction In recent years, there has been a growing interest in neuromorphic computing, a field that seeks to imitate the structure and functionality of the human brain in developing computer architectures. Inspired by the…

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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