title: Boolformer: Symbolic Regression of Logic Functions with Transformers
summary: 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.
paper_authors._id: 650d360cfc7c3a59df043a5b
paper_authors.name: Stéphane d'Ascoli
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2023-09-22T06:37:01.867Z
paper_authors._id: 64b69ca5d374717b636fd1cf
paper_authors.avatarUrl: /avatars/89c5b9076077c57c19f3512f8a41b88d.svg
paper_authors.fullname: Stéphane d'Ascoli
paper_authors.type: user
paper_authors.user: sdascoli
paper_authors._id: 650d360cfc7c3a59df043a5c
paper_authors.name: Samy Bengio
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-22T15:27:30.961Z
paper_authors._id: 6488d89678570cedad6f4e03
paper_authors.avatarUrl: /avatars/d3c86914cab46b953c0ee7ed17e3d6bb.svg
paper_authors.fullname: Samy Bengio
paper_authors.type: user
paper_authors.user: bengio
paper_authors._id: 650d360cfc7c3a59df043a5d
paper_authors.name: Josh Susskind
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-22T15:28:32.825Z
paper_authors._id: 6470d2247fd7ecdbd0ec3cc9
paper_authors.avatarUrl: /avatars/52c5eca12499a1aa9bd49c43d4f20685.svg
paper_authors.fullname: Joshua M. Susskind
paper_authors.type: user
paper_authors.user: jsusskind
paper_authors._id: 650d360cfc7c3a59df043a5e
paper_authors.name: Emmanuel Abbé
paper_title: Boolformer: Symbolic Regression of Logic Functions with Transformers
paper_summary: 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.
paper_ai_keywords: Transformer architecture symbolic regression Boolean functions truth table approximate expressions binary classification interpretable alternative genetic algorithms gene regulatory networks