Principles of EBMs
Energy-Based Models, championed by Yann LeCun, provide an alternative to traditional generative modeling. An EBM learns a scalar function that assigns a low "energy" value to compatible configurations of variables and a high value to incompatible ones.
Technical Characteristics
- Non-Probabilistic Foundations: EBMs avoid the computational cost of normalized probability distributions, making them more scalable for complex dependencies.
- Inference as Optimization: Reasoning is modeled as an energy minimization problem, where the goal is to find the variable state that optimizes the compatibility function.
Historical Context & Precursors
- Hopfield Networks (1982): Foundational associative memory models where states evolve toward local minima of an energy function.
- Boltzmann Machines: Stochastic versions of Hopfield networks that introduced hidden units and temperature-based annealing.
- Restricted Boltzmann Machines (RBMs): A simplified architecture used extensively in the early 2000s for deep belief networks and unsupervised feature learning.
- Harmoniums: Also known as undirected graphical models, representing an early framework for modeling joint distributions via potential functions.
Operational Utility
EBMs are applicable in scenarios requiring Constraint Satisfaction. Energy functions serve as validators for checking if proposed system states or agent actions are compatible with high-level logical or safety constraints.