Publication

Learning Reasoning Strategies in End-to-End Differentiable Proving

International Conference on Machine Learning (ICML)


Abstract

Attempts to render deep learning models interpretable, data-efficient, and robust have seen some success through hybridisation with rule-based systems like Neural Theorem Provers (NTPs). These neuro-symbolic reasoning models can induce interpretable rules and learn representations from data via back-propagation, while providing logical explanations for their predictions. However, they are restricted by their computational complexity, as they need to consider all possible proof paths for explaining a goal, thus rendering them unfit for large-scale applications. We present Conditional Theorem Provers (CTPs), an extension to NTPs that learns an optimal rule selection strategy via gradient-based optimisation. We show that CTPs are scalable and yield state-of-the-art results on the CLUTRR dataset, which tests systematic generalisation of neural models by learning to reason over smaller graphs, and evaluating on larger ones. Finally, CTPs show better link prediction results on standard benchmarks in comparison with other neuro-symbolic reasoning models, while retaining their explainability properties.

Related Publications

All Publications

MICCAI - October 5, 2020

Active MR k-space Sampling with Reinforcement Learning

Luis Pineda, Sumana Basu, Adriana Romero, Roberto Calandra, Michal Drozdzal

Multimodal Video Analysis Workshop at ECCV - August 23, 2020

Audio-Visual Instance Discrimination

Pedro Morgado, Nuno Vasconcelos, Ishan Misra

Interspeech - October 24, 2020

Efficient Wait-k Models for Simultaneous Machine Translation

Maha Elbayad, Laurent Besacier, Jakob Verbeek

ICML - November 3, 2020

Learning Near Optimal Policies with Low Inherent Bellman Error

Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy