Boolformer: Symbolic Regression of Logic Functions with Transformers
In this work, we introduce Boolformer, the first Transformer architecture
trained to perform end-to-end symbolic regression of Boolean functions. First,
we show that it can predict compact formulas for complex functions which were
not seen during training, when provided a clean truth table. Then, we
demonstrate its ability to find approximate expressions when provided
incomplete and noisy observations. We evaluate the Boolformer on a broad set of
real-world binary classification datasets, demonstrating its potential as an
interpretable alternative to classic machine learning methods. Finally, we
apply it to the widespread task of modelling the dynamics of gene regulatory
networks. Using a recent benchmark, we show that Boolformer is competitive with
state-of-the art genetic algorithms with a speedup of several orders of
magnitude. Our code and models are available publicly.
650d360cfc7c3a59df043a5b
Stéphane d'Ascoli
extracted_pending
2023-09-22T06:37:01.867Z
64b69ca5d374717b636fd1cf
/avatars/89c5b9076077c57c19f3512f8a41b88d.svg
Stéphane d'Ascoli
user
sdascoli
650d360cfc7c3a59df043a5c
Samy Bengio
admin_assigned
2023-09-22T15:27:30.961Z
6488d89678570cedad6f4e03
/avatars/d3c86914cab46b953c0ee7ed17e3d6bb.svg
Samy Bengio
user
bengio
650d360cfc7c3a59df043a5d
Josh Susskind
admin_assigned
2023-09-22T15:28:32.825Z
6470d2247fd7ecdbd0ec3cc9
/avatars/52c5eca12499a1aa9bd49c43d4f20685.svg
Joshua M. Susskind
user
jsusskind
650d360cfc7c3a59df043a5e
Emmanuel Abbé
Boolformer: Symbolic Regression of Logic Functions with Transformers
In this work, we introduce Boolformer, the first Transformer architecture
trained to perform end-to-end symbolic regression of Boolean functions. First,
we show that it can predict compact formulas for complex functions which were
not seen during training, when provided a clean truth table. Then, we
demonstrate its ability to find approximate expressions when provided
incomplete and noisy observations. We evaluate the Boolformer on a broad set of
real-world binary classification datasets, demonstrating its potential as an
interpretable alternative to classic machine learning methods. Finally, we
apply it to the widespread task of modelling the dynamics of gene regulatory
networks. Using a recent benchmark, we show that Boolformer is competitive with
state-of-the art genetic algorithms with a speedup of several orders of
magnitude. Our code and models are available publicly.
Boolformer, a Transformer architecture, achieves competitive symbolic regression of Boolean functions on real-world datasets and gene regulatory networks with significant speed improvements over genetic algorithms.
Transformer architecture symbolic regression Boolean functions truth table binary classification datasets genetic algorithms gene regulatory networks