Boolformer：使用Transformer進行邏輯函數的符號回歸

摘要

在這份工作中，我們介紹了 Boolformer，這是第一個 Transformer 結構，經過訓練可執行端對端的布林函數符號回歸。首先，我們展示它能夠在提供乾淨真值表時，預測複雜函數的簡潔公式，即使這些函數在訓練期間並未見過。然後，我們展示它在提供不完整和有噪音的觀測時，找到近似表達式的能力。我們在廣泛的現實世界二元分類數據集上評估 Boolformer，展示其作為可解釋替代傳統機器學習方法的潛力。最後，我們將其應用於建模基因調控網絡動態的常見任務。使用最新的基準測試，我們展示 Boolformer 與最先進的遺傳算法相比具有數量級的加速。我們的代碼和模型已公開提供。

English

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：使用Transformer進行邏輯函數的符號回歸

Boolformer: Symbolic Regression of Logic Functions with Transformers

摘要

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