Machine learning (ML) has enabled a whole host of innovations and new business models in fintech, driving breakthroughs in areas such as personalized wealth management, automated fraud detection, and real-time small business accounting tools. For a long time, one of the most significant challenges of machine learning has been the amount and quality of data that is required to train machine learning models. Recent developments of Transformer architectures, however, have started to change this equation. Transformer-based models like BERT (Bidirectional Encoder Representations from Transformers, developed at Google) and GPT (Generative Pre-Training, developed at OpenAI) have brought about the biggest changes in machine learning in recent years. These technologies were initially developed to process natural language data but are now creating exciting new opportunities across many applications, including fintech. Want to learn the fundamental building blocks of Data Modeling?

CNF-based SAT and MaxSAT solvers are central to logic synthesis and verification systems. The increasing popularity of these constraint problems in electronic design automation encourages studies on different SAT problems and their properties for further computational efficiency. There has been both theoretical and practical success of modern Conflict-driven clause learning SAT solvers, which allows solving very large industrial instances in a relatively short amount of time. Recently, machine learning approaches provide a new dimension to solving this challenging problem. Neural symbolic models could serve as generic solvers that can be specialized for specific domains based on data without any changes to the structure of the model. In this work, we propose a one-shot model derived from the Transformer architecture to solve the MaxSAT problem, which is the optimization version of SAT where the goal is to satisfy the maximum number of clauses. Our model has a scale-free structure which could process varying size of instances. We use meta-path and self-attention mechanism to capture interactions among homogeneous nodes. We adopt cross-attention mechanisms on the bipartite graph to capture interactions among heterogeneous nodes. We further apply an iterative algorithm to our model to satisfy additional clauses, enabling a solution approaching that of an exact-SAT problem. The attention mechanisms leverage the parallelism for speedup. Our evaluation indicates improved speedup compared to heuristic approaches and improved completion rate compared to machine learning approaches.