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MaRN (Mapping Networks) is a new open-source PyTorch library that trains neural networks by optimizing a compact latent representation instead of all parameters directly, showing up to 57.7× parameter reduction on MNIST with modest accuracy trade-offs. The author presents exploratory benchmarks and invites community feedback on the approach.
Introduces latent PDE mapping, a physics-informed learning technique that enables efficient geometric generalization with sparse training data by pulling back PDE residuals to a latent geometry. Demonstrates significant error reduction on cardiac electrophysiology PDE benchmarks using only 15 training samples.