Year: 2022
Type: article
Abstract: Symbolic Optimization has been used to solve varied challenging and relevant problems such as Symbolic Regression and Neural Architecture Search.However, the current state-of-the-art typically learns each problem from scratch, and is unable to leverage preexisting knowledge and datasets that are available for many applications.Inspired by the similarity between sequence representations learned in Natural Language Processing and the formulation of symbolic optimization as a discrete sequence optimization problem, we propose Language Model-Accelerated Deep Symbolic Optimization (LA-DSO), a method that leverages language models to learn symbolic optimization solutions more efficiently.We demonstrate LA-DSO in two tasks: symbolic regression, which allows us to compare against the state-of-the-art approaches, and computational antibody optimization, which shows that our proposal accelerates learning for challenging real-world problems.
Author: Silva, F L [Lawrence Livermore National Laboratory]; Petersen, B [Lawrence Livermore National Laboratory]; Faissol, D [Lawrence Livermore National Laboratory]
Institution: Lawrence Livermore National Laboratory
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4296154226