Abstract: Machine Learning (ML) algorithms require large datasets, often costly to create. This work proposes a generator model that builds synthetic texture datasets by synthesizing repeatable ...

Abstract: Real-world datasets often suffer from both noisy labels and imbalanced class distribution, presenting significant challenges for the effective deployment of deep neural networks (DNNs).

A comprehensive, production-ready framework for building self-improving AI agents with advanced features including polymorphic output, universal signal bus, agent brokerage, orchestration, constraint ...