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Residual networks for deep learning – Complete Phd and Masters Thesis

[ad_1] Introduction In recent years, deep learning has revolutionized various fields such as computer vision, natural language processing, and speech recognition. One of the challenges in deep learning is training very deep neural networks, as…

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…

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…

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…

Multi-agent reinforcement learning for coordination – Complete Phd and Masters Thesis

[ad_1] Introduction Multi-agent reinforcement learning (MARL) is a subfield of artificial intelligence that focuses on developing algorithms and techniques for coordinating multiple autonomous agents to achieve a common goal. The coordination of multiple agents presents…

Secure aggregation for distributed learning – Complete Phd and Masters Thesis

[ad_1] Introduction Secure aggregation for distributed learning is a critical component in the field of machine learning and data privacy. With the increasing amount of data being collected and processed in various applications, the need…

Secure multi-party computation for collaborative learning – Complete Phd and Masters Thesis

[ad_1] Introduction The increasing demand for data privacy and security in collaborative learning environments has led to the development of secure multi-party computation (MPC) techniques. These techniques allow multiple parties to jointly compute a function…

Tag: uw computer science masters