MaRN：通过低维参数映射训练神经网络的 PyTorch 库

I built MaRN: a PyTorch library for training neural networks through low-dimensional parameter mappings [P]

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开发者发布 PyTorch 库 MaRN（Mapping Networks），可优化紧凑的潜在表示而非直接训练全部模型参数。基准测试中，MNIST CNN 可训练参数从 107,998 降至 1,872（57.7 倍缩减），准确率 91.80%；LSTM 预测参数从 12,051 降至 2,048，验证 MSE 为 0.00006。映射模型训练速度明显更慢，且性能因任务而异，基准测试仍属探索性质。

正文

I built MaRN (Mapping Networks), a PyTorch library that lets you optimize a compact latent representation instead of directly training every model parameter.

Some results from my current benchmarks:

MNIST CNN: 107,998 → 1,872 trainable parameters (57.7× reduction), with 91.80% accuracy.
LSTM forecasting: 12,051 → 2,048 parameters, with validation MSE of 0.00006.
CNN2 + pruning: 204 trainable parameters, with 81.25% accuracy.

These results come with trade-offs: mapped models can train substantially slower, and performance varies by task. The benchmarks are exploratory, using synthetic data for some tasks, and aren't evidence of general superiority over direct training.

The library includes global and layer-wise mappings, regularization options, and pruning/LRD integrations.

Code: https://github.com/arjunmnath/MaRN
Docs: https://marn.readthedocs.io/

I'd value feedback on the approach, benchmark design, and where this kind of parameter-efficient optimization could be useful.

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