Modelos Baseados em Energia (EBMs) são um tipo de modelo probabilístico usada em aprendizado de máquina and inteligência artificial to represent complex distributions over data. They operate on the principle that each configuration of the model corresponds to an energy value, with lower energy indicating a more likely or favorable configuration. The key idea is to learn an energy function that assigns a scalar value to each possible data point, which can then be used to derive probabilities through normalization.
Em termos matemáticos, um EBM define uma probability distribution by associating an energy value, denoted as E(x), to each data point x. The probability of a particular data point is calculated using the Boltzmann distribution, which is expressed as:
P(x) = exp(-E(x)) / Z
Aqui, Z é a constante de normalização conhecida como a função de partição, which ensures that the probabilities sum to one across all configurations. Learning in EBMs typically involves optimizing the energy function, often using techniques like contrastive divergence or other sampling methods.
Os EBMs têm se mostrado eficazes em várias aplicações, incluindo geração de imagens, denoising, and as generative models for aprendizado não supervisionado. They can capture complex relationships in the data, making them a powerful tool in the field of deep learning and beyond.