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Found 3 papers, 1 papers with code
Automated Statistical Model Discovery with Language Models
We evaluate our method in three common settings in probabilistic modeling: searching within a restricted space of models, searching over an open-ended space, and improving classic models under natural language constraints (e. g., this model should be interpretable to an ecologist).
Why think step by step? Reasoning emerges from the locality of experience
We investigate why and how chain-of-thought reasoning is useful in language models, testing the hypothesis that reasoning is effective when training data consists of overlapping local clusters of variables that influence each other strongly.
Gaussian Process Surrogate Models for Neural Networks
Not being able to understand and predict the behavior of deep learning systems makes it hard to decide what architecture and algorithm to use for a given problem.
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