# Lattice-Based Pruning in Recurrent Neural Networks via Poset Modeling

arXiv: https://arxiv.org/abs/2502.16525 · HTML: https://mlsanity.com/paper/2502.16525/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 12 references that are in the map.
- First posted on arXiv 23 Feb 2025.

## Builds on (most cited first, 9 of 9)

- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [Learning Structured Sparsity in Deep Neural Networks](https://mlsanity.com/paper/1608.03665/) (2016)
- [Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science](https://mlsanity.com/paper/1707.04780/) (2017)
- [A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers](https://mlsanity.com/paper/1804.03294/) (2018)
- [Exploring Sparsity in Recurrent Neural Networks](https://mlsanity.com/paper/1704.05119/) (2017)
- [Methods for Pruning Deep Neural Networks](https://mlsanity.com/paper/2011.00241/) (2020)
- [Neural Graph Evolution: Towards Efficient Automatic Robot Design](https://mlsanity.com/paper/1906.05370/) (2019)
- [Lattice-Based Recurrent Neural Network Encoders for Neural Machine Translation](https://mlsanity.com/paper/1609.07730/) (2016)
- [Lattice physics approaches for neural networks](https://mlsanity.com/paper/2405.12022/) (2024)

## Abstract (arXiv)

Recurrent neural networks (RNNs) are central to sequence modeling tasks, yet their high computational complexity poses challenges for scalability and real-time deployment. Traditional pruning techniques, predominantly based on weight magnitudes, often overlook the intrinsic structural properties of these networks. We introduce a novel framework that models RNNs as partially ordered sets (posets) and constructs corresponding dependency lattices. By identifying meet irreducible neurons, our lattice-based pruning algorithm selectively retains critical connections while eliminating redundant ones. The method is implemented using both binary and continuous-valued adjacency matrices to capture different aspects of network connectivity. Evaluated on the MNIST dataset, our approach exhibits a clear trade-off between sparsity and classification accuracy. Moderate pruning maintains accuracy above 98%, while aggressive pruning achieves higher sparsity with only a modest performance decline. Unlike conventional magnitude-based pruning, our method leverages the structural organization of RNNs, resulting in more effective preservation of functional connectivity and improved efficiency in multilayer networks with top-down feedback. The proposed lattice-based pruning framework offers a rigorous and scalable approach for reducing RNN complexity while sustaining robust performance, paving the way for more efficient hierarchical models in both machine learning and computational neuroscience.