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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…

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…

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…

Bayesian neural networks for uncertainty estimation – Complete Phd and Masters Thesis

[ad_1] Introduction Bayesian neural networks (BNNs) have gained significant attention in recent years due to their ability to provide uncertainty estimates in neural network models. This is crucial in various applications such as medical diagnosis,…

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…

Triplet networks for comparative learning – Complete Phd and Masters Thesis

[ad_1] Introduction Triplet networks have gained popularity in recent years for their ability to learn effective representations for various tasks, particularly in the field of comparative learning. In a triplet network, the model is trained…

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…

Capsule networks for part-whole relationships – Complete Phd and Masters Thesis

[ad_1] Introduction Chapter 1: Introduction 1.1 The Introduction 1.2 Background of Study 1.3 Problem Statement 1.4 Objective of Study 1.5 Limitation of Study 1.6 Scope of Study 1.7 Significance of Study 1.8 Structure of the…

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