•1 min read•from Machine Learning
Visual Perceptual to Conceptual First-Order Rule Learning Networks [R]
Our take
In recent discussions surrounding Visual Perceptual to Conceptual First-Order Rule Learning Networks, the field of Inductive Logic Programming (ILP) appears to be gaining momentum, particularly in addressing challenges associated with pure image datasets and predicate induction. Historically viewed as a complex hurdle for ILP, recent papers suggest significant advancements in performance. This raises an intriguing question: can ILP effectively compete in domains dominated by deep learning and neural networks, such as machine vision? Exploring this potential could reshape our understanding of data-driven learning approaches.
I'm genuinely curious, because I've been seeing some papers come out recently from the ILP world, like referenced above as well as others [1, 2]. It seems they're busy cooking.
In the main linked paper they're tackling pure image datasets and predicate induction which I've previously read was very difficult for ILP. They're claiming strong performance.
Could ILP ever viably compete in DL/NN dominated spaces like machine vision, stable?
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