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Knowledge base question answering for querying – Complete Phd and Masters Thesis

[ad_1] Introduction: In recent years, Knowledge base question answering has become an essential tool for retrieving information from large scale databases. With the increasing amount of data available on the internet, traditional keyword-based search engines…

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…

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…

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…

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…

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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