# Compressing Recurrent Neural Networks with Tensor Ring for Action Recognition

arXiv: https://arxiv.org/abs/1811.07503 · HTML: https://mlsanity.com/paper/1811.07503/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2018.

## Built on by (most cited first, 30 of 34)

- [CHIP: CHannel Independence-based Pruning for Compact Neural Networks](https://mlsanity.com/paper/2110.13981/) (2021)
- [Towards Efficient Tensor Decomposition-Based DNN Model Compression with Optimization Framework](https://mlsanity.com/paper/2107.12422/) (2021)
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- [Tensor networks for interpretable and efficient quantum-inspired machine learning](https://mlsanity.com/paper/2311.11258/) (2023)
- [Measuring Social Norms of Large Language Models](https://mlsanity.com/paper/2404.02491/) (2024)
- [Reusing Pretrained Models by Multi-linear Operators for Efficient Training](https://mlsanity.com/paper/2310.10699/) (2023)
- [Triple-stream Deep Metric Learning of Great Ape Behavioural Actions](https://mlsanity.com/paper/2301.02642/) (2023)
- [Tensor Decomposition for Model Reduction in Neural Networks: A Review](https://mlsanity.com/paper/2304.13539/) (2023)
- [GOHSP: A Unified Framework of Graph and Optimization-based Heterogeneous Structured Pruning for Vision Transformer](https://mlsanity.com/paper/2301.05345/) (2023)
- [Preparing Lessons for Progressive Training on Language Models](https://mlsanity.com/paper/2401.09192/) (2024)
- [TedNet: A Pytorch Toolkit for Tensor Decomposition Networks](https://mlsanity.com/paper/2104.05018/) (2021)
- [Towards Extremely Compact RNNs for Video Recognition with Fully Decomposed Hierarchical Tucker Structure](https://mlsanity.com/paper/2104.05758/) (2021)
- [A Unified Weight Initialization Paradigm for Tensorial Convolutional Neural Networks](https://mlsanity.com/paper/2205.15307/) (2022)
- [On the Memory Mechanism of Tensor-Power Recurrent Models](https://mlsanity.com/paper/2103.01521/) (2021)
- [Going Deeper into Recognizing Actions in Dark Environments: A Comprehensive Benchmark Study](https://mlsanity.com/paper/2202.09545/) (2022)
- [Interaction Decompositions for Tensor Network Regression](https://mlsanity.com/paper/2208.06029/) (2022)
- [CSTAR: Towards Compact and STructured Deep Neural Networks with Adversarial Robustness](https://mlsanity.com/paper/2212.01957/) (2022)
- [Low-Rank Compression of Language Models via Differentiable Rank Selection](https://mlsanity.com/paper/2512.13733/) (2025)
- [How to Train Unstable Looped Tensor Network](https://mlsanity.com/paper/2203.02617/) (2022)
- [Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models](https://mlsanity.com/paper/2501.08727/) (2025)
- [Efficient Visual Recognition with Deep Neural Networks: A Survey on Recent Advances and New Directions](https://mlsanity.com/paper/2108.13055/) (2021)
- [Multiscale Tensor Summation Factorization as a New Neural Network Layer (MTS Layer) for Multidimensional Data Processing](https://mlsanity.com/paper/2504.13975/) (2025)
- [STN: Scalable Tensorizing Networks via Structure-Aware Training and Adaptive Compression](https://mlsanity.com/paper/2205.15198/) (2022)
- [Algorithm and Hardware Co-Design of Energy-Efficient LSTM Networks for Video Recognition with Hierarchical Tucker Tensor Decomposition](https://mlsanity.com/paper/2212.02046/) (2022)
- [On Model Compression for Neural Networks: Framework, Algorithm, and Convergence Guarantee](https://mlsanity.com/paper/2303.06815/) (2023)
- [Tensor Ring Optimized Quantum-Enhanced Tensor Neural Networks](https://mlsanity.com/paper/2310.01515/) (2023)
- [Long Sequence Modeling with Attention Tensorization: From Sequence to Tensor Learning](https://mlsanity.com/paper/2410.20926/) (2024)
- [Event-based Timestamp Image Encoding Network for Human Action Recognition and Anticipation](https://mlsanity.com/paper/2104.05145/) (2021)
- [Fast Hypergraph Regularized Nonnegative Tensor Ring Factorization Based on Low-Rank Approximation](https://mlsanity.com/paper/2109.02314/) (2021)

## Abstract (arXiv)

Recurrent Neural Networks (RNNs) and their variants, such as Long-Short Term Memory (LSTM) networks, and Gated Recurrent Unit (GRU) networks, have achieved promising performance in sequential data modeling. The hidden layers in RNNs can be regarded as the memory units, which are helpful in storing information in sequential contexts. However, when dealing with high dimensional input data, such as video and text, the input-to-hidden linear transformation in RNNs brings high memory usage and huge computational cost. This makes the training of RNNs unscalable and difficult. To address this challenge, we propose a novel compact LSTM model, named as TR-LSTM, by utilizing the low-rank tensor ring decomposition (TRD) to reformulate the input-to-hidden transformation. Compared with other tensor decomposition methods, TR-LSTM is more stable. In addition, TR-LSTM can complete an end-to-end training and also provide a fundamental building block for RNNs in handling large input data. Experiments on real-world action recognition datasets have demonstrated the promising performance of the proposed TR-LSTM compared with the tensor train LSTM and other state-of-the-art competitors.