Unsupervised Generative Models Reveal Number Sense Through Rate-Distortion Analysis

This research explores how unsupervised generative models develop an understanding of numerical concepts. The rate-distortion perspective provides a novel framework for analyzing the emergence of number sense in these models.

• •Applies rate-distortion theory to understand how unsupervised models learn numerical representations.
• •Investigates the emergence of 'number sense' without explicit supervision.
• •Provides a theoretical framework for analyzing and potentially improving model efficiency.