Neural algorithmic reasoning with causal regularisation
B Bevilacqua, K Nikiforou, B Ibarz… - International …, 2023 - proceedings.mlr.press
International Conference on Machine Learning, 2023•proceedings.mlr.press
Recent work on neural algorithmic reasoning has investigated the reasoning capabilities of
neural networks, effectively demonstrating they can learn to execute classical algorithms on
unseen data coming from the train distribution. However, the performance of existing neural
reasoners significantly degrades on out-of-distribution (OOD) test data, where inputs have
larger sizes. In this work, we make an important observation: there are many different inputs
for which an algorithm will perform certain intermediate computations identically. This insight …
neural networks, effectively demonstrating they can learn to execute classical algorithms on
unseen data coming from the train distribution. However, the performance of existing neural
reasoners significantly degrades on out-of-distribution (OOD) test data, where inputs have
larger sizes. In this work, we make an important observation: there are many different inputs
for which an algorithm will perform certain intermediate computations identically. This insight …
Abstract
Recent work on neural algorithmic reasoning has investigated the reasoning capabilities of neural networks, effectively demonstrating they can learn to execute classical algorithms on unseen data coming from the train distribution. However, the performance of existing neural reasoners significantly degrades on out-of-distribution (OOD) test data, where inputs have larger sizes. In this work, we make an important observation: there are many different inputs for which an algorithm will perform certain intermediate computations identically. This insight allows us to develop data augmentation procedures that, given an algorithm’s intermediate trajectory, produce inputs for which the target algorithm would have exactly the same next trajectory step. We ensure invariance in the next-step prediction across such inputs, by employing a self-supervised objective derived by our observation, formalised in a causal graph. We prove that the resulting method, which we call Hint-ReLIC, improves the OOD generalisation capabilities of the reasoner. We evaluate our method on the CLRS algorithmic reasoning benchmark, where we show up to 3x improvements on the OOD test data.
proceedings.mlr.press