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Semantic web for machine-readable knowledge – Complete Phd and Masters Thesis

[ad_1] Introduction Semantic web technology has emerged as a promising solution for organizing and extracting knowledge from the vast amount of information available on the internet. By using standardized metadata and ontologies, the semantic web…

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 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…

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,…

Differentiable neural computers for algorithmic reasoning – Complete Phd and Masters Thesis

[ad_1] Introduction Differentiable neural computers (DNCs) are a type of neural network that combines the power of traditional neural networks with the ability to store and retrieve information in an external memory matrix. This allows…

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…

Mixture of experts for specialized sub-networks – Complete Phd and Masters Thesis

[ad_1] Introduction Mixture of experts for specialized sub-networks is a cutting-edge approach in the field of machine learning and artificial intelligence that aims to improve the performance of neural networks by combining multiple specialized sub-networks…

Self-attention networks for global dependencies – Complete Phd and Masters Thesis

[ad_1] Introduction: Self-attention networks have gained significant attention in the field of artificial intelligence and machine learning due to their ability to capture global dependencies in data. These networks have shown promising results in various…

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