written by Dr. Maryam Miradi on Linkedin Visit her website https://www.maryammiradi.com/
Model uncertainty presents a significant challenge in deploying reliable deep learning systems.
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Model Uncertainty
It refers to the inability to predict model performance under different scenarios.
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Reason and Impact
Uncertain model predictions can lead to unreliable or unsafe application outcomes.
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Types of Model Uncertainty
✸ Epistemic Uncertainty: Due to limited data or knowledge.
✸ Aleatory Uncertainty: Inherent randomness in the data.
✸ Distributional Uncertainty: Changes in data distribution over time.
✸ Model Discrepancy: Mismatch between model assumptions and real-world dynamics.
✸ Structural Uncertainty: Inaccuracies in model structure affecting predictions.
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Solutions to Model Uncertainty
✺ Conformal Prediction: Provide valid prediction intervals under assumptions.
✺ Bayesian Neural Networks: Incorporate uncertainty in network weights.
✺ Ensemble Methods: Combine multiple models to reduce errors.
✺ Dropout as Bayesian Approximation: Use dropout to estimate uncertainty.
✺ Monte Carlo Methods: Sample predictions to gauge variability.
✺ Bootstrapping: Resample data to enhance model robustness.
✺ Adversarial Training: Train with perturbed inputs to improve resilience.
✺ Uncertainty Quantification: Measure and interpret model confidence.
✺ Out-of-Distribution Detection: Identify and handle novel data inputs.
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Python Libraries for Model Uncertainty Solutions
📚 ConformalPy: Simplifies implementing conformal prediction in Python.
📚 PyMC3: Bayesian modeling and probabilistic ML.
📚 TensorFlow Probability: for probabilistic reasoning and statistical analysis.
📚 scikit-learn: Ensemble methods.
📚 Keras: Easy implementation of dropout and custom layers.
📚 PyTorch: Facilitates dynamic NNs with dropout.
📚 Edward: probabilistic modeling, inference, and criticism.
📚 Torch Ensemble: Simplify ensemble methods in PyTorch.
📚 Foolbox: Crafting adversarial attacks.
📚 Uncertainty Toolbox: Quantifying, communicating, and visualizing uncertainty.
📚 Alibi-Detect: Detecting and handling out-of-distribution and adversarial instances.
📚 Bootstrap: Implementing bootstrapping to estimate statistical accuracy.
📚 MC Dropout: Applying Monte Carlo Dropout in neural networks.
📚 GPy: Gaussian processes for ML.
📚 DeepRobust: for adversarial attacks and defenses.
📚 Pyro: Deep probabilistic modeling using variational inference.
📚 Spektral: GNN library for uncertainty in structured data.
📚 PaddlePaddle: robust model development in deep learning.
📚 Chainer: Offers a flexible and intuitive approach to neural networks.
📚 Theano: Allows efficient symbolic differentiation and model evaluation.
📚 MAPE
🔗Sources:
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