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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…

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…

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…

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…

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…

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…

Convolutional neural networks for grid-like data – Complete Phd and Masters Thesis

[ad_1] Introduction Convolutional neural networks (CNNs) have gained significant importance in recent years for their ability to effectively extract and learn features from grid-like data such as images, videos, and sensor data. This thesis focuses…

Graph neural networks for structured data – Complete Phd and Masters Thesis

[ad_1] Introduction Graph neural networks have emerged as a powerful tool for analyzing and modeling structured data such as social networks, protein-protein interaction networks, and citation networks. Unlike traditional neural networks that operate on grid-like…

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