Title: RandRep: Creativity from Randomness
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Controlled Randomness, Creative Knowledge Discovery, Dual-Pathway Architecture, Optimal Transport Theory, Neural Network Creativity, Alternative Interpretation Learning, Multi-Objective Optimization

Abstract: The RandRep neural architecture uses controlled random elements to find new knowledge that exceeds what supervised learning methods can achieve. The system generates multiple correct solutions for ambiguous data through its structured method which includes controlled randomness. The architecture enables pattern detection through its combination of deterministic feature extraction with adaptive random vector processing and temperature scheduling and dual-head uncertainty quantification which preserves classification accuracy. The RandRep memory buffering system functions as a scientific discovery workflow simulator which retains acquired knowledge to guide upcoming exploration activities. The RandRep model achieves 81\% accuracy in text classification tasks on the AG News dataset while identifying 38 new patterns which make up 38\% of its total creative alternative interpretations. The mathematical framework shows information gain convergence when using optimal transport constraints and experimental results show that controlled randomness uncovers semantic patterns which standard deterministic methods cannot detect. Neural networks gain creative problem-solving capabilities through the strategic implementation of randomness which allows them to solve problems beyond their training data for unsupervised learning and scientific discovery and creative artificial intelligence systems.

TL;DR: Random Representative Learning : RandRep