A naukri.com initiative
Arxiv
1d
30
Image Credit: Arxiv
Learning richness modulates equality reasoning in neural networks
- Equality reasoning is a common and abstract concept that can be evaluated regardless of the objects involved.
- Same-different (SD) tasks are extensively studied to understand abstract reasoning in humans and animals.
- Neural networks have shown proficiency in abstract reasoning, sparking interest in studying equality reasoning in these models.
- However, there is little consensus on conclusions regarding equality reasoning in neural networks.
- A theory of equality reasoning in multi-layer perceptrons (MLP) is developed to clarify the principles in learning SD tasks.
- Two types of behaviors, conceptual and perceptual, are identified in relation to equality reasoning.
- Conceptual behavior is task-specific, efficient in learning, and less affected by irrelevant details.
- Perceptual behavior is highly sensitive to irrelevant details and requires exhaustive training to learn the task.
- The behavior of an MLP in equality reasoning tasks is driven by learning richness, categorized as rich or lazy regimes.
- Rich-regime MLPs exhibit conceptual behavior, while lazy-regime MLPs exhibit perceptual behavior.
- Experimental validation in vision SD tasks shows that rich feature learning enhances success by promoting conceptual behavior.
- Learning richness in feature learning is identified as a critical factor influencing equality reasoning.
- The study suggests that equality reasoning in humans and animals could also depend on learning richness in neural circuits.
Read Full Article
1 Like
For uninterrupted reading, download the app