low-dimensional parameter mappings

Train a neural network with 57x fewer parameters using this new PyTorch library

MaRN takes a different path to parameter efficiency: instead of training every weight directly, it optimizes a compact latent representation that maps to the full network. The numbers are striking, a 57.7× reduction on an MNIST CNN, but the honesty about trade-offs is what makes this worth attention. Slower training and task-dependent performance are real constraints, not footnotes. For exploratory benchmarks, that transparency matters more than the headline reductions. The library's global and layer-wise mappings give researchers room to test where this approach genuinely fits.