Basic Concepts

The main goal of Bensemble is to provide a unified interface for different ensembling and Bayesian methods.

The Ensemble Container

Everything revolves around the Ensemble class. It acts as a manager for multiple "members" (individual neural networks).

• Explicit Ensembles: A collection of different models (e.g., from NAS or Deep Ensembles).
• Implicit Ensembles: A single model that behaves like an ensemble (e.g., MC Dropout or Bayesian layers).

Regardless of the source, an Ensemble always returns a tensor of shape [M, Batch, Output], where M is the number of ensemble members.