Regularizing Explanations in Bayesian Convolutional Neural Networks

Understand

Neural networks are powerful function approximators with tremendous potential in learning complex distributions.

• However, they are prone to overfitting on spurious patterns.
• Bayesian inference provides a principled way to regularize neural networks and give well-calibrated uncertainty estimates.
• It allows us to specify prior knowledge on weights.