1 citations · 3 across the 6 of their papers we have counts for
6 papers
Are Object-Centric Representations Better At Compositional Generalization?
Ferdinand Kapl, Amir Mohammad Karimi Mamaghan, Maximilian Seitzer +4
Compositional generalization, the ability to reason about novel combinations of familiar concepts, is fundamental to human cognition and a critical challenge for machine learning.…
From Words to Amino Acids: Does the Curse of Depth Persist?
Aleena Siji, Amir Mohammad Karimi Mamaghan, Ferdinand Kapl +9
Protein language models (PLMs) have become widely adopted as general-purpose models, demonstrating strong performance in protein engineering and de novo design. Like large language…
A Centralized Planning and Distributed Execution Method for Shape Filling with Homogeneous Mobile Robots
Shuqing Liu, Rong Su, Karl H. Johansson
The pattern formation task is commonly seen in a multi-robot system. In this paper, we study the problem of forming complex shapes with functionally limited mobile robots, which ha…
Exploring the Effectiveness of Object-Centric Representations in Visual Question Answering: Comparative Insights with Foundation Models
Amir Mohammad Karimi Mamaghan, Samuele Papa, Karl Henrik Johansson +2
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…
Challenges and Considerations in the Evaluation of Bayesian Causal Discovery
Amir Mohammad Karimi Mamaghan, Panagiotis Tigas, Karl Henrik Johansson +3
Representing uncertainty in causal discovery is a crucial component for experimental design, and more broadly, for safe and reliable causal decision making. Bayesian Causal Discove…
Diffusion Based Causal Representation Learning
Amir Mohammad Karimi Mamaghan, Andrea Dittadi, Stefan Bauer +2
Causal reasoning can be considered a cornerstone of intelligent systems. Having access to an underlying causal graph comes with the promise of cause-effect estimation and the ident…