%0 Generic %1 dascoli2022symbolic %A d'Ascoli, Stéphane %A Kamienny, Pierre-Alexandre %A Lample, Guillaume %A Charton, François %D 2022 %K ak-symbolic-numeric deep-learning from:adulny maths recurrence-prediction sem_ws23 student-paper symbolic-regression transformer %T Deep Symbolic Regression for Recurrent Sequences %U http://arxiv.org/abs/2201.04600 %X 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 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, e.g. $bessel0(x)\approx \sin(x)+\cos(x)x$ and $1.644934\pi^2/6$. An interactive demonstration of our models is provided at https://symbolicregression.metademolab.com.

@misc{dascoli2022symbolic, abstract = {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 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, e.g. $\operatorname{bessel0}(x)\approx \frac{\sin(x)+\cos(x)}{\sqrt{\pi x}}$ and $1.644934\approx \pi^2/6$. An interactive demonstration of our models is provided at https://symbolicregression.metademolab.com.}, added-at = {2023-10-23T11:50:54.000+0200}, author = {d'Ascoli, Stéphane and Kamienny, Pierre-Alexandre and Lample, Guillaume and Charton, François}, biburl = {https://www.bibsonomy.org/bibtex/2097a699f27ce448a496b23296c19d9a4/adulny}, description = {[2201.04600] Deep Symbolic Regression for Recurrent Sequences}, interhash = {1ba41a8896b486dea83d15676afb5104}, intrahash = {097a699f27ce448a496b23296c19d9a4}, keywords = {ak-symbolic-numeric deep-learning from:adulny maths recurrence-prediction sem_ws23 student-paper symbolic-regression transformer}, note = {cite arxiv:2201.04600}, timestamp = {2023-10-30T15:03:27.000+0100}, title = {Deep Symbolic Regression for Recurrent Sequences}, url = {http://arxiv.org/abs/2201.04600}, year = 2022 }