K.R.: Deep Boltzmann machines and the centering trick

Abstract. Deep Boltzmann machines are in theory capable of learning efficient representations of seemingly complex data. Designing an algo-rithm that effectively learns the data representation can be subject to multiple difficulties. In this chapter, we present the “centering trick ” that consists of rewriting the energy of the system as a function of centered states. The centering trick improves the conditioning of the underlying optimization problem and makes learning more stable, leading to models with better generative and discriminative properties