Neural Conditional Transport Maps

1. 1Define the problem as a conditional optimal transport problem
2. 2Implement a hypernetwork to generate transport layer parameters based on conditioning variables
3. 3Train the hypernetwork using a suitable loss function and optimization algorithm
4. 4Evaluate the performance of the learned transport maps on a test dataset

AI engineers and researchers on a team can benefit from this framework as it enables adaptive mappings that outperform simpler conditioning methods, and data scientists can apply it to various problems involving probability distributions

💡 The framework introduces a conditioning mechanism that can process both categorical and continuous conditioning variables simultaneously

🚀 Neural Conditional Transport Maps: a new framework for learning conditional optimal transport maps between probability distributions