Effective Mean-Field Inference Method for Nonnegative Boltzmann Machines
This work addresses inference challenges in probabilistic models for multi-modal nonnegative data, which is incremental as it builds on recent methods like the diagonal consistency technique.
The authors tackled the problem of inference in nonnegative Boltzmann machines by proposing an effective mean-field method, achieving improved accuracy and computational efficiency compared to existing approaches.
Nonnegative Boltzmann machines (NNBMs) are recurrent probabilistic neural network models that can describe multi-modal nonnegative data. NNBMs form rectified Gaussian distributions that appear in biological neural network models, positive matrix factorization, nonnegative matrix factorization, and so on. In this paper, an effective inference method for NNBMs is proposed that uses the mean-field method, referred to as the Thouless--Anderson--Palmer equation, and the diagonal consistency method, which was recently proposed.