“Deep Symbolic Regression for Recurrent Sequences”, 2022-01-12 (; similar):
Symbolic regression, i.e. predicting a function from the observation of its values, is well-known to be a challenging task.
In this paper, we train Transformers to infer the function or recurrence relation underlying sequences of integers or floats, a typical task in human IQ tests which has hardly been tackled in the machine learning literature.
We evaluate our integer model on a subset of OEIS sequences, and show that it outperforms built-in Wolfram Mathematica functions for recurrence prediction. We also demonstrate that our float model is able to yield informative approximations of out-of-vocabulary functions and constants, eg.
bessel0(x) ≈ sin(x)+cos(x) / √πx and 1.644934 ≈ π2⁄6.An interactive demonstration of our models is provided at [broken URL].