A type of model with machine learning (ML) and artificial intelligence (AI) that conceptualizes the learning process as finding a configuration of the system’s variables that minimizes an energy function. This concept is borrowed from physics, where systems move towards lower energy states.
- Energy Function: This function assigns a scalar energy value to each configuration of the variables in the model. Lower energy values are associated with more probable configurations, making the energy function central to the model’s operation.
- Learning: In EBMs, learning involves adjusting the energy function parameters to decrease the energy of correct or desired outcomes and increase it for incorrect or undesired ones.
- Inference: This is the process of finding the configuration of variables that minimizes the energy. This can be challenging, especially in complex models, and often requires sophisticated optimization techniques.
EBMs provide a unified framework that can incorporate different types of data and model complex dependencies between variables. EBMs are versatile and can be applied to various tasks in machine learning, including:
- Image recognition: Identifying objects and patterns within images.
- Natural language processing (NLP): Understanding and generating human language.
- Anomaly detection: Identifying unusual patterns or data points.
The main challenge with EBMs is computational, as optimizing the energy function can be difficult, especially for large and complex models. Additionally, designing an appropriate energy function requires deep domain knowledge and expertise.