A Decision Tree for Practitioners Needing Uncertainty Quantification for Their Deep Learning Project

Welcome to the documentation for the UQ Decision Tree paper.

Overview

This paper provides a practical decision tree to guide machine learning practitioners in selecting appropriate uncertainty quantification (UQ) methods for their deep learning projects.

Contents

• Introduction
  • Background
  • Motivation
  • Scope
  • Target Audience
  • Organization of This Paper
• Methodology
  • Developing the Decision Tree
  • Key Decision Factors
  • Evaluation Criteria
  • Method Categories
• Decision Tree
  • The UQ Decision Framework
  • Decision Tree Structure
  • Detailed Method Selection
  • Implementation Considerations
• Case Studies
  • Real-World Applications of the Decision Tree
  • Case Study 1: Medical Image Classification
  • Case Study 2: Autonomous Vehicle Perception
  • Case Study 3: Financial Forecasting
  • Case Study 4: Natural Language Processing
  • Lessons Learned
• Conclusions
  • Summary
  • Key Contributions
  • Recommendations for Practitioners
  • Limitations and Future Work
  • Call to Action
  • Final Thoughts
  • Acknowledgments
  • Contact
• References
  • Key Papers on Uncertainty Quantification
  • Software and Tools
  • Online Resources
  • Datasets and Benchmarks

Quick Links

• Introduction

• Methodology

• Decision Tree

• Case Studies

• Conclusions

Indices and tables

• Index

• Search Page