Energy-Based Models (EBMs) are a class of models in machine learning that provide a framework for capturing complex relationships in data. They model the system by assigning a energy to each possible configuration of variables, which are used to distinguish outcomes and correct data points from less desirable or unlikely ones. EBMs are well-suited for tasks where ranking between many possible outcomes is required, as they rely on energy scores to derive probabilities using the Gibbs or Boltzmann distribution.
However, the computational cost of computing the normalization constant (Z(x)) can be extremely high or even intractable for large output spaces, a central challenge in using EBMs.
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