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Found 4 papers, 2 papers with code
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity
We present an improved method for symbolic regression that seeks to fit data to formulas that are Pareto-optimal, in the sense of having the best accuracy for a given complexity.
Structured, flexible, and robust: benchmarking and improving large language models towards more human-like behavior in out-of-distribution reasoning tasks
We first contribute a new challenge benchmark for comparing humans and distributional large language models (LLMs).
How do Language Models Bind Entities in Context?
To correctly use in-context information, language models (LMs) must bind entities to their attributes.
Learning adaptive planning representations with natural language guidance
Effective planning in the real world requires not only world knowledge, but the ability to leverage that knowledge to build the right representation of the task at hand.
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