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Causal inference for understanding relationships – Complete Phd and Masters Thesis

[ad_1] Introduction Causal inference is a crucial aspect of research that helps us understand the relationships between variables and determine cause-and-effect relationships. By identifying the causal relationships between variables, researchers can make informed decisions and…

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…

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,…

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…

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…

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…

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…

Siamese networks for similarity learning – Complete Phd and Masters Thesis

[ad_1] **Introduction** Siamese networks have gained significant attention in recent years for their ability to learn similarity between pairs of inputs in a variety of domains such as image recognition, natural language processing, and recommendation…

Tag: 1 year masters in computer science