Sparsifying Bayesian neural networks with latent binary variables and normalizing flows

Understand

Artificial neural networks (ANNs) are powerful machine learning methods used in many modern applications such as facial recognition, machine translation, and cancer diagnostics.

• A common issue with ANNs is that they usually have millions or billions of trainable parameters, and therefore tend to overfit to the training data.
• This is especially problematic in applications where it is important to have reliable uncertainty estimates.
• Bayesian neural networks (BNN) can improve on this, since they incorporate parameter uncertainty.