Deep Symbolic Optimization for Scientific Discovery
Date:
Symposium Talk at AAAI 2023 Spring Symposium on Computational Approaches to Scientific Discovery, Hyatt Regency, San Francisco Airport, California
Symposium talk on deep symbolic optimization as a framework for scientific discovery across symbolic regression, control, and other structured design problems. DSO casts the search for compact symbolic solutions, such as equations or control policies, as a sequential decision problem in which a neural network generates candidates that are rewarded by how well they explain data or solve a task. The talk surveyed the main components of the framework and highlighted results such as the unified deep symbolic regression method that won the real-world track of the 2022 SRBench symbolic regression competition.
Slides from the talk and the framework’s foundational paper are available below: Slides (PDF) · Symposium page · A Unified Framework for Deep Symbolic Regression, NeurIPS 2022