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probly 0.3.1 documentation
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Content

  • Introduction
  • The probly Python Package
  • Core Concepts
  • Main Components
  • Advanced Topics
  • Examples and Tutorials
  • Implemented methods
  • Contributing Guide
  • References and Further Reading
  • FAQ and Troubleshooting

Notebooks

  • Notebook Examples
    • Utilities and Layers
      • Custom Loss Functions
      • Evaluation Metrics
      • Key Probabilistic Layers in probly
      • Utility Functions
    • Evaluation and Quantification
      • Credal Sets Visualization Plotting
      • A tutorial for clustervisualiser.py
      • Dirichlet distribution tutorial
      • Calibration Metrics
      • Interpretation techniques
      • Visualisation Tools
    • 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)
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Utilities and LayersΒΆ

  • Custom Loss Functions
  • Evaluation Metrics
  • Key Probabilistic Layers in probly
  • Utility Functions
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Custom Loss Functions
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