arXiv (All)
open access
Improving Neural Symbolic Regression with a Diverse Prior and Post-training Alignment
Symbolic regression is the problem of finding an expression that fits a dataset. Solving it requires searching a combinatorial space of candidate expressions. Neural symbolic regression can amortize that search by training a model once on synthetic data and then predicting expressions for a new data
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Exhaustive Canonical Skeletons Generation: A Formal Dataset for Decomposable Symbolic Regression#1356175
- Not Every Term Adds New Structure: Sobolev Novelty for Symbolic Regression#1116928
- A-SR: Self-Evolving Agentic LLMs for Symbolic Regression via Hierarchical Coordination#429380
- Predict in Chunks, Revise in Spans: History-conditioned Action Regeneration for Robotic Manipulation#1325760
- Vision-Predictor-Action: Towards Efficient Embodied AI via Neuro-Symbolic JEPA Predictors#1326806
Record · ID 1343192
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive