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Counterfactual reasoning for alternative scenarios – Complete Phd and Masters Thesis

[ad_1] Introduction Counterfactual reasoning is a powerful cognitive tool that allows individuals to explore hypothetical scenarios and analyze the outcomes that could have occurred under different circumstances. In the field of decision making and policy…

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…

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…

Federated neural networks for distributed learning – Complete Phd and Masters Thesis

[ad_1] Introduction Federated learning is a decentralized machine learning approach that allows multiple parties to collaboratively train a global model without sharing their data. This approach addresses privacy concerns and data security issues associated with…

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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