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World Congress 2021 • Jul 1, 2021

Uncertainty Estimation of Neural Networks

Tillman Radmer , Fabian Hüger , Nico Schmidt

Why do neural networks make dangerously overconfident predictions in novel situations? Learn to calculate true model uncertainty to build safer, self-aware autonomous systems.

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#1 about 4 min

Familiarizing with machine learning and neural network basics

Assessing general audience knowledge on foundational machine learning and neural network concepts.

#2 about 5 min

Differentiating between aleatoric and epistemic uncertainty

Analyzing how measurement noise and rare events contribute differently to model uncertainty.

#3 about 4 min

Estimating uncertainty using classification scores and calibration

Standard soft-max scores function poorly as uncertainty predictors unless corrected through output calibration.

#4 about 2 min

Predicting neural network failures with secondary alert models

Secondary alert models offer an inexpensive method to anticipate when a base neural network might fail.

#5 about 8 min

Using dropout sampling to estimate neural network uncertainty

Bayesian approximations implemented via network dropout generate a distribution of predictions to measure system uncertainty.

#6 about 8 min

Identifying automotive applications for uncertainty estimation

Crowdsourced audience input categorizes essential use cases for uncertainty tracking within advanced driver assistance systems.

#7 about 7 min

Managing continuous sensor data with corner case detection

Active learning loops rely on predefined trigger functions to filter informative corner cases from massive sensor data environments.

#8 about 8 min

Improving segmentation models with uncertainty-based data selection

Calculating component-wise uncertainty on semantic segmentation outputs highlights priority edge cases required for model retraining.

#9 about 5 min

Evaluating dataset bias and active learning framework weaknesses

Static datasets and generalized evaluation workflows restrict the overall effectiveness of uncertainty-based data selection frameworks.

#10 about 7 min

Ensuring verifiable artificial intelligence safety in autonomous driving

Recognizing insufficient generalization and unreliable confidence measures is necessary for establishing verifiable neural network safety standards.

#11 about 6 min

Developing safety augmentation processes for neural network deployments

Iterative safety augmentation pipelines and model-driven architectures reduce operational risks within open-world autonomous environments.

#12 about 4 min

Transitioning into the automotive artificial intelligence safety field

Software engineers can transition into artificial intelligence safety by applying proven development practices to analytical systems.

Matching moments

2:52 min

Introduction to safety-critical machine learning in automotive contexts

Jan Zawadzki · WWC 2022

4:55 min

Audience Q&A on autonomous driving models and data

Liang Yu · WWC 2022

1:51 min

Establishing comprehensive safety assurance cases for operational machine learning

Jan Zawadzki · WWC 2022

7:31 min

Addressing participant questions on liability and machine learning

Georg Kühberger +1 · LIVE

2:25 min

Navigating automotive complexity with AI runtime environments

Daniel Graff +1 · WWC 2021

2:09 min

Speaker background and the need for robust models

Svetlin Penkov · LIVE

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