Instruction Set and Language for Symbolic Regression
Instruction Set and Language for Symbolic Regression explores A novel representation framework for symbolic regression that encodes expression DAGs as strings and computes a pruned canonical string to collapse equivalent representations.. Commercial viability score: 2/10 in Symbolic Regression.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Prior WorkReinforcement Learning with Symbolic Reward MachinesViability: 2.0
- Higher ViabilityPrior-Guided Symbolic Regression: Towards Scientific Consistency in Equation DiscoveryViability: 3.0
- Higher ViabilityExplaining the Explainer: Understanding the Inner Workings of Transformer-based Symbolic Regression ModelsViability: 4.0
- Higher ViabilitySymbol-Equivariant Recurrent Reasoning ModelsViability: 6.0
- Higher ViabilitySymbolic--KAN: Kolmogorov-Arnold Networks with Discrete Symbolic Structure for Interpretable Learning