Efficient Training of Energy-Based Models Using Jarzynski Equality

Energy-based models (EBMs) are generative models inspired by statistical physics with a wide range of applications in unsupervised learning. Their performance is well measured by the cross-entropy (CE) of the model distribution relative to the data distribution. Using the CE as the objective for training is however challenging because the computation of its gradient with respect to the model parameters requires sampling the model distribution. Here we show how results for nonequilibrium thermodynamics based on Jarzynski equality together with tools from sequential Monte-Carlo sampling can be used to perform this computation efficiently and avoid the uncontrolled approximations made using the standard contrastive divergence algorithm. Specifically, we introduce a modification of the unadjusted Langevin algorithm (ULA) in which each walker acquires a weight that enables the estimation of the gradient of the cross-entropy at any step during GD, thereby bypassing sampling biases induced by slow mixing of ULA. We illustrate these results with numerical experiments on Gaussian mixture distributions as well as the MNIST and CIFAR-10 datasets. We show that the proposed approach outperforms methods based on the contrastive divergence algorithm in all the considered situations. Probabilistic models have become a key tool in generative artificial intelligence (AI) and unsupervised learning. Their goal is twofold: explain the training data, and allow the synthesis of new samples. Many flavors have been introduced in the last decades, including variational auto-encoders [1, 2, 3] generative adversarial networks [4, 5] , normalizing flows [6, 7, 8, 9, 10] , diffusion-based models [11, 12, 13] , restricted Boltzmann machines [14, 15, 16] , and energy-based models (EBMs) [17, 18, 19] . 37th Conference on Neural Information Processing Systems (NeurIPS 2023).