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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, including language modeling, speech synthesis, and image generation. In this thesis, we focus on autoregressive models for sequential generation, where the model generates a sequence of data one step at a time based on previous steps.

Chapter One: 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
1.9 Definition of terms

Chapter Two: Literature Review
2.1 Introduction to autoregressive models
2.2 Previous studies on autoregressive models for sequential generation
2.3 Applications of autoregressive models in different domains
2.4 Comparison of autoregressive models with other generative models
2.5 Training techniques for autoregressive models
2.6 Evaluation metrics for autoregressive models
2.7 Challenges and limitations of autoregressive models
2.8 Future directions in autoregressive models research
2.9 Summary of the literature review

Chapter Three: System Design and Methodology
3.1 Introduction
3.2 Data preprocessing for autoregressive models
3.3 Model architectures for autoregressive generation
3.4 Training algorithms for autoregressive models
3.5 Hyperparameter tuning for autoregressive models
3.6 Evaluation methods for autoregressive models
3.7 Performance optimization techniques for autoregressive models
3.8 Comparison with other generative models
3.9 Summary of system design and methodology

Chapter Four: System Implementation
4.1 Introduction
4.2 Implementation of data preprocessing techniques
4.3 Implementation of autoregressive model architectures
4.4 Training the autoregressive models
4.5 Fine-tuning and optimization of the models
4.6 Evaluation of the models
4.7 Comparison with baseline models
4.8 Discussion of implementation results
4.9 Summary of system implementation

Chapter Five: Conclusion and Summary
5.1 Conclusion
5.2 Summary of findings
5.3 Contributions of the thesis
5.4 Future directions for research in autoregressive models for sequential generation

Thesis Overview

Autoregressive models have shown great potential in generating sequential data, such as text, audio, and images. In this thesis, we explore the use of autoregressive models for sequential generation and investigate their effectiveness in various applications. The literature review provides a comprehensive overview of previous studies and applications of autoregressive models, highlighting the strengths and limitations of these models. The system design and methodology chapter discusses the data preprocessing, model architectures, training algorithms, and evaluation methods used in the study. The system implementation chapter details the implementation of the models, including data preprocessing, model training, optimization, and evaluation. Finally, the conclusion and summary chapter provides a summary of the findings, contributions of the thesis, and suggestions for future research directions in autoregressive models for sequential generation.

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