Exploring the Synergy of Symbolic and Neural Approaches: Advancements in Neurosymbolic Generative Models for Complex Data Structures and Applications

Abstract

In this paper, we explore the synergy of symbolic and neural approaches in the context of neurosymbolic generative models. These models integrate the strengths of symbolic models in representing abstract concepts and logical reasoning with the learning and generalization capabilities of neural models, opening new avenues for dealing with complex data structures and applications. We delve into various aspects of neurosymbolic generative models, including neurosymbolic programming, generative models for discrete structures, probabilistic circuits and tractable models, causality-inspired generative models, and their applications in natural language processing, computer vision, programming, and robotics. Furthermore, we discuss the evaluation and comparison of neurosymbolic generative models to regular generative models, highlighting their potential benefits and challenges. By investigating the advancements in neurosymbolic generative models, we aim to shed light on the future of this interdisciplinary research area and its potential impact on various fields.