Mode-Assisted Unsupervised Learning of Restricted Boltzmann Machines
This addresses a key bottleneck in unsupervised learning for researchers and practitioners using energy-based models, offering an incremental improvement to existing gradient methods.
The paper tackles the difficulty of training Restricted Boltzmann Machines (RBMs) by introducing mode training, which combines standard gradient updates with an off-gradient direction using RBM ground state samples, resulting in faster training, improved stability, and lower converged KL divergence, as demonstrated on synthetic datasets and MNIST.
Restricted Boltzmann machines (RBMs) are a powerful class of generative models, but their training requires computing a gradient that, unlike supervised backpropagation on typical loss functions, is notoriously difficult even to approximate. Here, we show that properly combining standard gradient updates with an off-gradient direction, constructed from samples of the RBM ground state (mode), improves their training dramatically over traditional gradient methods. This approach, which we call mode training, promotes faster training and stability, in addition to lower converged relative entropy (KL divergence). Along with the proofs of stability and convergence of this method, we also demonstrate its efficacy on synthetic datasets where we can compute KL divergences exactly, as well as on a larger machine learning standard, MNIST. The mode training we suggest is quite versatile, as it can be applied in conjunction with any given gradient method, and is easily extended to more general energy-based neural network structures such as deep, convolutional and unrestricted Boltzmann machines.