Sparse Relational Reasoning with Object-Centric Representations
Alex F. Spies, Alessandra Russo, Murray Shanahan

TL;DR
This paper explores how increasing sparsity in relational neural networks with object-centric representations affects their performance and interpretability, revealing trade-offs and failure modes.
Contribution
It systematically studies the impact of sparsity constraints on object-centric relational models, highlighting benefits and limitations.
Findings
Sparsity improves model performance and simplifies relations.
Object-centric representations can be detrimental if objects are not fully captured.
Trade-offs exist between interpretability and performance.
Abstract
We investigate the composability of soft-rules learned by relational neural architectures when operating over object-centric (slot-based) representations, under a variety of sparsity-inducing constraints. We find that increasing sparsity, especially on features, improves the performance of some models and leads to simpler relations. Additionally, we observe that object-centric representations can be detrimental when not all objects are fully captured; a failure mode to which CNNs are less prone. These findings demonstrate the trade-offs between interpretability and performance, even for models designed to tackle relational tasks.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsBayesian Modeling and Causal Inference · Topic Modeling · Machine Learning and Data Classification
