Generative Marginalization Models

• URL: http://arxiv.org/abs/2310.12920v1
• Date: Thu, 19 Oct 2023 17:14:29 GMT
• Title: Generative Marginalization Models
• Authors: Sulin Liu, Peter J. Ramadge, Ryan P. Adams
• Abstract summary: marginalization models (MaMs) are a new family of generative models for high-dimensional discrete data. They offer scalable and flexible generative modeling with tractable likelihoods. For energy-based training tasks, MaMs enable any-order generative modeling of high-dimensional problems.
• Score: 24.694121731706314
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce marginalization models (MaMs), a new family of generative models for high-dimensional discrete data. They offer scalable and flexible generative modeling with tractable likelihoods by explicitly modeling all induced marginal distributions. Marginalization models enable fast evaluation of arbitrary marginal probabilities with a single forward pass of the neural network, which overcomes a major limitation of methods with exact marginal inference, such as autoregressive models (ARMs). We propose scalable methods for learning the marginals, grounded in the concept of "marginalization self-consistency". Unlike previous methods, MaMs support scalable training of any-order generative models for high-dimensional problems under the setting of energy-based training, where the goal is to match the learned distribution to a given desired probability (specified by an unnormalized (log) probability function such as energy function or reward function). We demonstrate the effectiveness of the proposed model on a variety of discrete data distributions, including binary images, language, physical systems, and molecules, for maximum likelihood and energy-based training settings. MaMs achieve orders of magnitude speedup in evaluating the marginal probabilities on both settings. For energy-based training tasks, MaMs enable any-order generative modeling of high-dimensional problems beyond the capability of previous methods. Code is at https://github.com/PrincetonLIPS/MaM.

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