DeepAI AI Chat
Log In Sign Up

Learning Structured Declarative Rule Sets – A Challenge for Deep Discrete Learning

by   Johannes Fürnkranz, et al.

Arguably the key reason for the success of deep neural networks is their ability to autonomously form non-linear combinations of the input features, which can be used in subsequent layers of the network. The analogon to this capability in inductive rule learning is to learn a structured rule base, where the inputs are combined to learn new auxiliary concepts, which can then be used as inputs by subsequent rules. Yet, research on rule learning algorithms that have such capabilities is still in their infancy, which is - we would argue - one of the key impediments to substantial progress in this field. In this position paper, we want to draw attention to this unsolved problem, with a particular focus on previous work in predicate invention and multi-label rule learning


page 1

page 2

page 3

page 4


Learning Interpretable Rules for Multi-label Classification

Multi-label classification (MLC) is a supervised learning problem in whi...

An Empirical Investigation into Deep and Shallow Rule Learning

Inductive rule learning is arguably among the most traditional paradigms...

Integrative Windowing

In this paper we re-investigate windowing for rule learning algorithms. ...

Conformal Rule-Based Multi-label Classification

We advocate the use of conformal prediction (CP) to enhance rule-based m...

Neuro-symbolic Rule Learning in Real-world Classification Tasks

Neuro-symbolic rule learning has attracted lots of attention as it offer...

Which Learning Algorithms Can Generalize Identity-Based Rules to Novel Inputs?

We propose a novel framework for the analysis of learning algorithms tha...

Learning Hierarchically Structured Concepts

We study the question of how concepts that have structure get represente...