Exploring Abstract Reasoning Methods Working with Disentangled Visual Attributes
Loading...
Author / Producer
Date
2023
Publication Type
Master Thesis
ETH Bibliography
yes
Citations
Altmetric
Data
Rights / License
Abstract
The capacity for abstract reasoning is a cornerstone of human intelligence, and replicating this in machine learning models presents a complex challenge. This study focuses on the RAVEN dataset, a standard for assessing abstract reasoning abilities. Building upon the foundational work of Hersche et al., which employed Vector Symbolic Architectures (VSA) for reasoning, we delve deeper into making the reasoning mechanism learnable. Central to our exploration is the introduction of the AxR-Learners Framework, which incorporates the Learnable Formula. This innovative model is designed to autonomously adapt, allowing for a more flexible and tailored approach to abstract reasoning tasks. Our findings highlight the efficacy of the Learnable Formula in enhancing machine reasoning, suggesting a promising avenue for the evolution of more sophisticated artificial intelligence systems.
Permanent link
Publication status
published
External links
Editor
Contributors
Book title
Journal / series
Volume
Pages / Article No.
Publisher
ETH Zurich
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
09462 - Hofmann, Thomas / Hofmann, Thomas