STANLEY: Stochastic Gradient Anisotropic Langevin Dynamics for Learning Energy-Based Models

• URL: http://arxiv.org/abs/2310.12667v1
• Date: Thu, 19 Oct 2023 11:55:16 GMT
• Title: STANLEY: Stochastic Gradient Anisotropic Langevin Dynamics for Learning Energy-Based Models
• Authors: Belhal Karimi, Jianwen Xie, Ping Li
• Abstract summary: We present an end-to-end learning algorithm for Energy-Based models (EBM) We propose in this paper, a novel high dimensional sampling method, based on an anisotropic stepsize and a gradient-informed covariance matrix. Our resulting method, namely STANLEY, is an optimization algorithm for training Energy-Based models via our newly introduced MCMC method.
• Score: 41.031470884141775
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose in this paper, STANLEY, a STochastic gradient ANisotropic LangEvin dYnamics, for sampling high dimensional data. With the growing efficacy and potential of Energy-Based modeling, also known as non-normalized probabilistic modeling, for modeling a generative process of different natures of high dimensional data observations, we present an end-to-end learning algorithm for Energy-Based models (EBM) with the purpose of improving the quality of the resulting sampled data points. While the unknown normalizing constant of EBMs makes the training procedure intractable, resorting to Markov Chain Monte Carlo (MCMC) is in general a viable option. Realizing what MCMC entails for the EBM training, we propose in this paper, a novel high dimensional sampling method, based on an anisotropic stepsize and a gradient-informed covariance matrix, embedded into a discretized Langevin diffusion. We motivate the necessity for an anisotropic update of the negative samples in the Markov Chain by the nonlinearity of the backbone of the EBM, here a Convolutional Neural Network. Our resulting method, namely STANLEY, is an optimization algorithm for training Energy-Based models via our newly introduced MCMC method. We provide a theoretical understanding of our sampling scheme by proving that the sampler leads to a geometrically uniformly ergodic Markov Chain. Several image generation experiments are provided in our paper to show the effectiveness of our method.

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