Notebook Examples

• Utilities and Layers
• Evaluation and Quantification
• Batch Ensemble Networks
• Bayesian Transformation
• Dropconnect Transformation
• Dropout Transformation
• Ensemble vs SubEnsemble Notebook
• Ensemble Transformation
• Potential Advantages of Ensembling Random Forests
• 2. Theoretical Background
• 3. Data Generation
• 4. Random Forest Ensemble Prototype
• 5. Uncertainty Analysis
• 6. Performance Metrics
• 7. Conclusion
• Evidential Classification Transformation
• Evidential Regression Transformation
• Out-of-Distribution Detection with an Ensemble
• Calibration with Label Relaxation
• Lazy Dispatch Test
• Multilib Demo
• A Brief Introduction to PyTraverse
• Uncertainty Quantification using scikit-learn-Ensembles
• Using probly with scikit-learn
• Sub-Ensembles for Fast Uncertainty Estimation
• Uncertainty for a Synthetic Regression Task using probly
• Calibration using Temperature Scaling
• Bayesian Neural Networks
• Evidential Model for Classification
• Evidential Regression Model
• Transformation Comparison: Dropout vs DropConnect vs Ensemble vs Bayesian vs Evidential (PyTorch)