Reparameterization Tricks: Variance Reduction by Smarter Gradients

TL;DR

Reparameterization tricks effectively reduce variance in gradient estimators by moving randomness outside the computation graph, resulting in smoother and more reliable training processes.

Key Takeaways

• Use reparameterization to reduce gradient noise in VAEs.
• Implement pathwise gradients for more stable training.
• Explore continuous relaxation methods for discrete variables.
• Apply variance reduction techniques to improve model performance.