[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…
Read More
[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…
Read More
[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…
Read More
[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…
Read More
[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…
Read More
[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…
Read More
[ad_1] Introduction: Chapter 1: Introduction 1.1 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 Thesis…
Read More
[ad_1] Introduction Normalizing flows have emerged as a powerful tool for flexible density estimation in recent years. These methods allow for the modeling of complex, multi-modal distributions by transforming a simple base distribution into the…
Read More
[ad_1] Introduction: Generative adversarial networks (GANs) have emerged as powerful tools for generating realistic synthetic data in recent years. By pitting two neural networks against each other in a zero-sum game setting, GANs are able…
Read More
[ad_1] Introduction: In recent years, there has been a growing interest in developing robust machine learning models that can perform well in the presence of adversarial attacks. These attacks are designed to exploit vulnerabilities in…
Read More