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Quantum neural networks for exponential speedup – Complete Phd and Masters Thesis

[ad_1] Introduction Quantum computing has emerged as a revolutionary technology with the potential to solve complex problems that are intractable for classical computers. In recent years, there has been significant interest in leveraging the power…

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…

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…

Transformers for sequence modeling – Complete Phd and Masters Thesis

[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…

Autoregressive models for sequential generation – Complete Phd and Masters Thesis

[ad_1] Introduction Autoregressive models have gained significant attention in recent years due to their ability to generate realistic sequences of data, such as text, audio, and images. These models have been successful in various applications,…

Generative adversarial networks for realistic synthesis – Complete Phd and Masters Thesis

[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…

Adversarial learning for robust models – Complete Phd and Masters Thesis

[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…

Game-theoretic learning for strategic interaction – Complete Phd and Masters Thesis

[ad_1] Introduction Game theory is a branch of applied mathematics that examines strategic interactions between rational decision-makers. Through the use of mathematical models, game theory seeks to understand the strategies that players use to maximize…

Tag: iprojectmaster computer science