SPM: Efficient Linear Transformations for Neural Networks
Analysis
This paper introduces Stagewise Pairwise Mixers (SPM) as a more efficient and structured alternative to dense linear layers in neural networks. By replacing dense matrices with a composition of sparse pairwise-mixing stages, SPM reduces computational and parametric costs while potentially improving generalization. The paper's significance lies in its potential to accelerate training and improve performance, especially on structured learning problems, by offering a drop-in replacement for a fundamental component of many neural network architectures.
Key Takeaways
- •SPM offers a computationally efficient alternative to dense linear layers.
- •SPM reduces both computational and parametric costs.
- •SPM can be a drop-in replacement for dense layers.
- •SPM may improve generalization on structured learning problems.
Reference
“SPM layers implement a global linear transformation in $O(nL)$ time with $O(nL)$ parameters, where $L$ is typically constant or $log_2n$.”