Gradient-free variational learning with conditional mixture networks

Understand

Balancing computational efficiency with robust predictive performance is crucial in supervised learning, especially for critical applications.

• Standard deep learning models, while accurate and scalable, often lack probabilistic features like calibrated predictions and uncertainty quantification.
• Bayesian methods address these issues but can be computationally expensive as model and data complexity increase.
• Previous work shows that fast variational methods can reduce the compute requirements of Bayesian methods by eliminating the need for gradient computation or sampling, but are often limited to simple models.