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Amir Mohammad Karimi Mamaghan

3 accepted papers

2026

Are Object-Centric Representations Better At Compositional Generalization?

ICML 2026poster

Compositional generalization, the ability to reason about novel combinations of familiar concepts, is fundamental to human cognition and a critical challenge for machine learning. Object-centric (OC) representations, which encode a scene as a set of objects, are often argued to support such generali…

Cited by 0SourceScholar
2025

Exploring the Effectiveness of Object-Centric Representations in Visual Question Answering: Comparative Insights with Foundation Models

ICLR 2025poster

Object-centric (OC) representations, which model visual scenes as compositions of discrete objects, have the potential to be used in various downstream tasks to achieve systematic compositional generalization and facilitate reasoning. However, these claims have yet to be thoroughly validated empiric…

Cited by 8SourcePDFScholar
2024

Challenges and Considerations in the Evaluation of Bayesian Causal Discovery

ICML 2024poster

Representing uncertainty in causal discovery is a crucial component for experimental design, and more broadly, for safe and reliable causal decision making. Bayesian Causal Discovery (BCD) offers a principled approach to encapsulating this uncertainty. Unlike non-Bayesian causal discovery, which rel…

Cited by 4SourcePDFScholar